Observer-based distributed adaptive iterative learning control for linear multi-agent systems

NASA Astrophysics Data System (ADS)

Li, Jinsha; Liu, Sanyang; Li, Junmin

2017-10-01

This paper investigates the consensus problem for linear multi-agent systems from the viewpoint of two-dimensional systems when the state information of each agent is not available. Observer-based fully distributed adaptive iterative learning protocol is designed in this paper. A local observer is designed for each agent and it is shown that without using any global information about the communication graph, all agents achieve consensus perfectly for all undirected connected communication graph when the number of iterations tends to infinity. The Lyapunov-like energy function is employed to facilitate the learning protocol design and property analysis. Finally, simulation example is given to illustrate the theoretical analysis.

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2018-02-01

This paper proposes a combined Virtual Reference Feedback Tuning-Q-learning model-free control approach, which tunes nonlinear static state feedback controllers to achieve output model reference tracking in an optimal control framework. The novel iterative Batch Fitted Q-learning strategy uses two neural networks to represent the value function (critic) and the controller (actor), and it is referred to as a mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach. Learning convergence of the Q-learning schemes generally depends, among other settings, on the efficient exploration of the state-action space. Handcrafting test signals for efficient exploration is difficult even for input-output stable unknown processes. Virtual Reference Feedback Tuning can ensure an initial stabilizing controller to be learned from few input-output data and it can be next used to collect substantially more input-state data in a controlled mode, in a constrained environment, by compensating the process dynamics. This data is used to learn significantly superior nonlinear state feedback neural networks controllers for model reference tracking, using the proposed Batch Fitted Q-learning iterative tuning strategy, motivating the original combination of the two techniques. The mixed Virtual Reference Feedback Tuning-Batch Fitted Q-learning approach is experimentally validated for water level control of a multi input-multi output nonlinear constrained coupled two-tank system. Discussions on the observed control behavior are offered. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Iterative learning control with applications in energy generation, lasers and health care.

PubMed

Rogers, E; Tutty, O R

2016-09-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability.

Effects of iterative learning based signal control strategies on macroscopic fundamental diagrams of urban road networks

NASA Astrophysics Data System (ADS)

Yan, Fei; Tian, Fuli; Shi, Zhongke

2016-10-01

Urban traffic flows are inherently repeated on a daily or weekly basis. This repeatability can help improve the traffic conditions if it is used properly by the control system. In this paper, we propose a novel iterative learning control (ILC) strategy for traffic signals of urban road networks using the repeatability feature of traffic flow. To improve the control robustness, the ILC strategy is further integrated with an error feedback control law in a complementary manner. Theoretical analysis indicates that the ILC-based traffic signal control methods can guarantee the asymptotic learning convergence, despite the presence of modeling uncertainties and exogenous disturbances. Finally, the impacts of the ILC-based signal control strategies on the network macroscopic fundamental diagram (MFD) are examined. The results show that the proposed ILC-based control strategies can homogenously distribute the network accumulation by controlling the vehicle numbers in each link to the desired levels under different traffic demands, which can result in the network with high capacity and mobility.

Iterative learning control with applications in energy generation, lasers and health care

PubMed Central

Tutty, O. R.

2016-01-01

Many physical systems make repeated executions of the same finite time duration task. One example is a robot in a factory or warehouse whose task is to collect an object in sequence from a location, transfer it over a finite duration, place it at a specified location or on a moving conveyor and then return for the next one and so on. Iterative learning control was especially developed for systems with this mode of operation and this paper gives an overview of this control design method using relatively recent relevant applications in wind turbines, free-electron lasers and health care, as exemplars to demonstrate its applicability. PMID:27713654

Language Evolution by Iterated Learning with Bayesian Agents

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Kalish, Michael L.

2007-01-01

Languages are transmitted from person to person and generation to generation via a process of iterated learning: people learn a language from other people who once learned that language themselves. We analyze the consequences of iterated learning for learning algorithms based on the principles of Bayesian inference, assuming that learners compute…

Learner-Controlled Scaffolding Linked to Open-Ended Problems in a Digital Learning Environment

ERIC Educational Resources Information Center

Edson, Alden Jack

2017-01-01

This exploratory study reports on how students activated learner-controlled scaffolding and navigated through sequences of connected problems in a digital learning environment. A design experiment was completed to (re)design, iteratively develop, test, and evaluate a digital version of an instructional unit focusing on binomial distributions and…

Designing the Architecture of Hierachical Neural Networks Model Attention, Learning and Goal-Oriented Behavior

DTIC Science & Technology

1993-12-31

19,23,25,26,27,28,32,33,35,41]) - A new cost function is postulated and an algorithm that employs this cost function is proposed for the learning of...updates the controller parameters from time to time [53]. The learning control algorithm consist of updating the parameter estimates as used in the...proposed cost function with the other learning type algorithms , such as based upon learning of iterative tasks [Kawamura-85], variable structure

Robust iterative learning contouring controller with disturbance observer for machine tool feed drives.

PubMed

Simba, Kenneth Renny; Bui, Ba Dinh; Msukwa, Mathew Renny; Uchiyama, Naoki

2018-04-01

In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Realization of Comfortable Massage by Using Iterative Learning Control Based on EEG

NASA Astrophysics Data System (ADS)

Teramae, Tatsuya; Kushida, Daisuke; Takemori, Fumiaki; Kitamura, Akira

Recently the massage chair is used by a lot of people because they are able to use it easily at home. However a present massage chair only realizes the massage motion. Moreover the massage chair can not consider the user’s condition and massage force. On the other hand, the professional masseur is according to presume the mental condition by patient’s reaction. Then this paper proposes the method of applying masseur’s procedure for the massage chair using iterative learning control based on EEG. And massage force is estimated by acceleration sensor. The realizability of the proposed method is verified by the experimental works using the massage chair.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

Model-Free Primitive-Based Iterative Learning Control Approach to Trajectory Tracking of MIMO Systems With Experimental Validation.

PubMed

Radac, Mircea-Bogdan; Precup, Radu-Emil; Petriu, Emil M

2015-11-01

This paper proposes a novel model-free trajectory tracking of multiple-input multiple-output (MIMO) systems by the combination of iterative learning control (ILC) and primitives. The optimal trajectory tracking solution is obtained in terms of previously learned solutions to simple tasks called primitives. The library of primitives that are stored in memory consists of pairs of reference input/controlled output signals. The reference input primitives are optimized in a model-free ILC framework without using knowledge of the controlled process. The guaranteed convergence of the learning scheme is built upon a model-free virtual reference feedback tuning design of the feedback decoupling controller. Each new complex trajectory to be tracked is decomposed into the output primitives regarded as basis functions. The optimal reference input for the control system to track the desired trajectory is next recomposed from the reference input primitives. This is advantageous because the optimal reference input is computed straightforward without the need to learn from repeated executions of the tracking task. In addition, the optimization problem specific to trajectory tracking of square MIMO systems is decomposed in a set of optimization problems assigned to each separate single-input single-output control channel that ensures a convenient model-free decoupling. The new model-free primitive-based ILC approach is capable of planning, reasoning, and learning. A case study dealing with the model-free control tuning for a nonlinear aerodynamic system is included to validate the new approach. The experimental results are given.

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

PubMed

Lewis, F L; Vamvoudakis, Kyriakos G

2011-02-01

Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

Networked iterative learning control design for discrete-time systems with stochastic communication delay in input and output channels

NASA Astrophysics Data System (ADS)

Liu, Jian; Ruan, Xiaoe

2017-07-01

This paper develops two kinds of derivative-type networked iterative learning control (NILC) schemes for repetitive discrete-time systems with stochastic communication delay occurred in input and output channels and modelled as 0-1 Bernoulli-type stochastic variable. In the two schemes, the delayed signal of the current control input is replaced by the synchronous input utilised at the previous iteration, whilst for the delayed signal of the system output the one scheme substitutes it by the synchronous predetermined desired trajectory and the other takes it by the synchronous output at the previous operation, respectively. In virtue of the mathematical expectation, the tracking performance is analysed which exhibits that for both the linear time-invariant and nonlinear affine systems the two kinds of NILCs are convergent under the assumptions that the probabilities of communication delays are adequately constrained and the product of the input-output coupling matrices is full-column rank. Last, two illustrative examples are presented to demonstrate the effectiveness and validity of the proposed NILC schemes.

Open-closed-loop iterative learning control for a class of nonlinear systems with random data dropouts

NASA Astrophysics Data System (ADS)

Cheng, X. Y.; Wang, H. B.; Jia, Y. L.; Dong, YH

2018-05-01

In this paper, an open-closed-loop iterative learning control (ILC) algorithm is constructed for a class of nonlinear systems subjecting to random data dropouts. The ILC algorithm is implemented by a networked control system (NCS), where only the off-line data is transmitted by network while the real-time data is delivered in the point-to-point way. Thus, there are two controllers rather than one in the control system, which makes better use of the saved and current information and thereby improves the performance achieved by open-loop control alone. During the transfer of off-line data between the nonlinear plant and the remote controller data dropout occurs randomly and the data dropout rate is modeled as a binary Bernoulli random variable. Both measurement and control data dropouts are taken into consideration simultaneously. The convergence criterion is derived based on rigorous analysis. Finally, the simulation results verify the effectiveness of the proposed method.

Active flow separation control by a position-based iterative learning control algorithm with experimental validation

NASA Astrophysics Data System (ADS)

Cai, Zhonglun; Chen, Peng; Angland, David; Zhang, Xin

2014-03-01

A novel iterative learning control (ILC) algorithm was developed and applied to an active flow control problem. The technique uses pulsed air jets to delay flow separation on a two-element high-lift wing. The ILC algorithm uses position-based pressure measurements to update the actuation. The method was experimentally tested on a wing model in a 0.9 m × 0.6 m low-speed wind tunnel at the University of Southampton. Compressed air and fast switching solenoid valves were used as actuators to excite the flow, and the pressure distribution around the chord of the wing was measured as a feedback control signal for the ILC controller. Experimental results showed that the actuation was able to delay the separation and increase the lift by approximately 10%-15%. By using the ILC algorithm, the controller was able to find the optimum control input and maintain the improvement despite sudden changes of the separation position.

Off-Policy Integral Reinforcement Learning Method to Solve Nonlinear Continuous-Time Multiplayer Nonzero-Sum Games.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai

2017-03-01

This paper establishes an off-policy integral reinforcement learning (IRL) method to solve nonlinear continuous-time (CT) nonzero-sum (NZS) games with unknown system dynamics. The IRL algorithm is presented to obtain the iterative control and off-policy learning is used to allow the dynamics to be completely unknown. Off-policy IRL is designed to do policy evaluation and policy improvement in the policy iteration algorithm. Critic and action networks are used to obtain the performance index and control for each player. The gradient descent algorithm makes the update of critic and action weights simultaneously. The convergence analysis of the weights is given. The asymptotic stability of the closed-loop system and the existence of Nash equilibrium are proved. The simulation study demonstrates the effectiveness of the developed method for nonlinear CT NZS games with unknown system dynamics.

Conceptual design of data acquisition and control system for two Rf driver based negative ion source for fusion R&D

NASA Astrophysics Data System (ADS)

Soni, Jigensh; Yadav, R. K.; Patel, A.; Gahlaut, A.; Mistry, H.; Parmar, K. G.; Mahesh, V.; Parmar, D.; Prajapati, B.; Singh, M. J.; Bandyopadhyay, M.; Bansal, G.; Pandya, K.; Chakraborty, A.

2013-02-01

Twin Source - An Inductively coupled two RF driver based 180 kW, 1 MHz negative ion source experimental setup is initiated at IPR, Gandhinagar, under Indian program, with the objective of understanding the physics and technology of multi-driver coupling. Twin Source [1] (TS) also provides an intermediate platform between operational ROBIN [2] [5] and eight RF drivers based Indian test facility -INTF [3]. A twin source experiment requires a central system to provide control, data acquisition and communication interface, referred as TS-CODAC, for which a software architecture similar to ITER CODAC core system has been decided for implementation. The Core System is a software suite for ITER plant system manufacturers to use as a template for the development of their interface with CODAC. The ITER approach, in terms of technology, has been adopted for the TS-CODAC so as to develop necessary expertise for developing and operating a control system based on the ITER guidelines as similar configuration needs to be implemented for the INTF. This cost effective approach will provide an opportunity to evaluate and learn ITER CODAC technology, documentation, information technology and control system processes, on an operational machine. Conceptual design of the TS-CODAC system has been completed. For complete control of the system, approximately 200 Nos. control signals and 152 acquisition signals are needed. In TS-CODAC, control loop time required is within the range of 5ms - 10 ms, therefore for the control system, PLC (Siemens S-7 400) has been chosen as suggested in the ITER slow controller catalog. For the data acquisition, the maximum sampling interval required is 100 micro second, and therefore National Instruments (NI) PXIe system and NI 6259 digitizer cards have been selected as suggested in the ITER fast controller catalog. This paper will present conceptual design of TS -CODAC system based on ITER CODAC Core software and applicable plant system integration processes.

Flyback CCM inverter for AC module applications: iterative learning control and convergence analysis

NASA Astrophysics Data System (ADS)

Lee, Sung-Ho; Kim, Minsung

2017-12-01

This paper presents an iterative learning controller (ILC) for an interleaved flyback inverter operating in continuous conduction mode (CCM). The flyback CCM inverter features small output ripple current, high efficiency, and low cost, and hence it is well suited for photovoltaic power applications. However, it exhibits the non-minimum phase behaviour, because its transfer function from control duty to output current has the right-half-plane (RHP) zero. Moreover, the flyback CCM inverter suffers from the time-varying grid voltage disturbance. Thus, conventional control scheme results in inaccurate output tracking. To overcome these problems, the ILC is first developed and applied to the flyback inverter operating in CCM. The ILC makes use of both predictive and current learning terms which help the system output to converge to the reference trajectory. We take into account the nonlinear averaged model and use it to construct the proposed controller. It is proven that the system output globally converges to the reference trajectory in the absence of state disturbances, output noises, or initial state errors. Numerical simulations are performed to validate the proposed control scheme, and experiments using 400-W AC module prototype are carried out to demonstrate its practical feasibility.

Approximate dynamic programming for optimal stationary control with control-dependent noise.

PubMed

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

Fuzzy adaptive iterative learning coordination control of second-order multi-agent systems with imprecise communication topology structure

NASA Astrophysics Data System (ADS)

Chen, Jiaxi; Li, Junmin

2018-02-01

In this paper, we investigate the perfect consensus problem for second-order linearly parameterised multi-agent systems (MAS) with imprecise communication topology structure. Takagi-Sugeno (T-S) fuzzy models are presented to describe the imprecise communication topology structure of leader-following MAS, and a distributed adaptive iterative learning control protocol is proposed with the dynamic of leader unknown to any of the agent. The proposed protocol guarantees that the follower agents can track the leader perfectly on [0,T] for the consensus problem. Under alignment condition, a sufficient condition of the consensus for closed-loop MAS is given based on Lyapunov stability theory. Finally, a numerical example and a multiple pendulum system are given to illustrate the effectiveness of the proposed algorithm.

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

NASA Astrophysics Data System (ADS)

Radac, Mircea-Bogdan; Precup, Radu-Emil; Roman, Raul-Cristian

2017-04-01

This paper proposes the combination of two model-free controller tuning techniques, namely linear virtual reference feedback tuning (VRFT) and nonlinear state-feedback Q-learning, referred to as a new mixed VRFT-Q learning approach. VRFT is first used to find stabilising feedback controller using input-output experimental data from the process in a model reference tracking setting. Reinforcement Q-learning is next applied in the same setting using input-state experimental data collected under perturbed VRFT to ensure good exploration. The Q-learning controller learned with a batch fitted Q iteration algorithm uses two neural networks, one for the Q-function estimator and one for the controller, respectively. The VRFT-Q learning approach is validated on position control of a two-degrees-of-motion open-loop stable multi input-multi output (MIMO) aerodynamic system (AS). Extensive simulations for the two independent control channels of the MIMO AS show that the Q-learning controllers clearly improve performance over the VRFT controllers.

A Control Systems Concept Inventory Test Design and Assessment

ERIC Educational Resources Information Center

Bristow, M.; Erkorkmaz, K.; Huissoon, J. P.; Jeon, Soo; Owen, W. S.; Waslander, S. L.; Stubley, G. D.

2012-01-01

Any meaningful initiative to improve the teaching and learning in introductory control systems courses needs a clear test of student conceptual understanding to determine the effectiveness of proposed methods and activities. The authors propose a control systems concept inventory. Development of the inventory was collaborative and iterative. The…

Performance improvement of robots using a learning control scheme

NASA Technical Reports Server (NTRS)

Krishna, Ramuhalli; Chiang, Pen-Tai; Yang, Jackson C. S.

1987-01-01

Many applications of robots require that the same task be repeated a number of times. In such applications, the errors associated with one cycle are also repeated every cycle of the operation. An off-line learning control scheme is used here to modify the command function which would result in smaller errors in the next operation. The learning scheme is based on a knowledge of the errors and error rates associated with each cycle. Necessary conditions for the iterative scheme to converge to zero errors are derived analytically considering a second order servosystem model. Computer simulations show that the errors are reduced at a faster rate if the error rate is included in the iteration scheme. The results also indicate that the scheme may increase the magnitude of errors if the rate information is not included in the iteration scheme. Modification of the command input using a phase and gain adjustment is also proposed to reduce the errors with one attempt. The scheme is then applied to a computer model of a robot system similar to PUMA 560. Improved performance of the robot is shown by considering various cases of trajectory tracing. The scheme can be successfully used to improve the performance of actual robots within the limitations of the repeatability and noise characteristics of the robot.

Proposing a new iterative learning control algorithm based on a non-linear least square formulation - Minimising draw-in errors

NASA Astrophysics Data System (ADS)

Endelt, B.

2017-09-01

Forming operation are subject to external disturbances and changing operating conditions e.g. new material batch, increasing tool temperature due to plastic work, material properties and lubrication is sensitive to tool temperature. It is generally accepted that forming operations are not stable over time and it is not uncommon to adjust the process parameters during the first half hour production, indicating that process instability is gradually developing over time. Thus, in-process feedback control scheme might not-be necessary to stabilize the process and an alternative approach is to apply an iterative learning algorithm, which can learn from previously produced parts i.e. a self learning system which gradually reduces error based on historical process information. What is proposed in the paper is a simple algorithm which can be applied to a wide range of sheet-metal forming processes. The input to the algorithm is the final flange edge geometry and the basic idea is to reduce the least-square error between the current flange geometry and a reference geometry using a non-linear least square algorithm. The ILC scheme is applied to a square deep-drawing and the Numisheet’08 S-rail benchmark problem, the numerical tests shows that the proposed control scheme is able control and stabilise both processes.

Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy.

PubMed

Liu, Hui; Li, Yingzi; Zhang, Yingxu; Chen, Yifu; Song, Zihang; Wang, Zhenyu; Zhang, Suoxin; Qian, Jianqiang

2018-01-01

Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to the sample topography. This method gets enough information about the output signals of PID controller and tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve good tracking performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Upper limb stroke rehabilitation: the effectiveness of Stimulation Assistance through Iterative Learning (SAIL).

PubMed

Meadmore, Katie L; Cai, Zhonglun; Tong, Daisy; Hughes, Ann-Marie; Freeman, Chris T; Rogers, Eric; Burridge, Jane H

2011-01-01

A novel system has been developed which combines robotic therapy with electrical stimulation (ES) for upper limb stroke rehabilitation. This technology, termed SAIL: Stimulation Assistance through Iterative Learning, employs advanced model-based iterative learning control (ILC) algorithms to precisely assist participant's completion of 3D tracking tasks with their impaired arm. Data is reported from a preliminary study with unimpaired participants, and also from a single hemiparetic stroke participant with reduced upper limb function who has used the system in a clinical trial. All participants completed tasks which involved moving their (impaired) arm to follow an image of a slowing moving sphere along a trajectory. The participants' arm was supported by a robot and ES was applied to the triceps brachii and anterior deltoid muscles. During each task, the same tracking trajectory was repeated 6 times and ILC was used to compute the stimulation signals to be applied on the next iteration. Unimpaired participants took part in a single, one hour training session and the stroke participant undertook 18, 1 hour treatment sessions composed of tracking tasks varying in length, orientation and speed. The results reported describe changes in tracking ability and demonstrate feasibility of the SAIL system for upper limb rehabilitation. © 2011 IEEE

Optimization Control of the Color-Coating Production Process for Model Uncertainty

PubMed Central

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results. PMID:27247563

Optimization Control of the Color-Coating Production Process for Model Uncertainty.

PubMed

He, Dakuo; Wang, Zhengsong; Yang, Le; Mao, Zhizhong

2016-01-01

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke

PubMed Central

2012-01-01

Background Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients’ voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Methods Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants’ arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. Results From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. Conclusions The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this. PMID:22676920

Functional electrical stimulation mediated by iterative learning control and 3D robotics reduces motor impairment in chronic stroke.

PubMed

Meadmore, Katie L; Hughes, Ann-Marie; Freeman, Chris T; Cai, Zhonglun; Tong, Daisy; Burridge, Jane H; Rogers, Eric

2012-06-07

Novel stroke rehabilitation techniques that employ electrical stimulation (ES) and robotic technologies are effective in reducing upper limb impairments. ES is most effective when it is applied to support the patients' voluntary effort; however, current systems fail to fully exploit this connection. This study builds on previous work using advanced ES controllers, and aims to investigate the feasibility of Stimulation Assistance through Iterative Learning (SAIL), a novel upper limb stroke rehabilitation system which utilises robotic support, ES, and voluntary effort. Five hemiparetic, chronic stroke participants with impaired upper limb function attended 18, 1 hour intervention sessions. Participants completed virtual reality tracking tasks whereby they moved their impaired arm to follow a slowly moving sphere along a specified trajectory. To do this, the participants' arm was supported by a robot. ES, mediated by advanced iterative learning control (ILC) algorithms, was applied to the triceps and anterior deltoid muscles. Each movement was repeated 6 times and ILC adjusted the amount of stimulation applied on each trial to improve accuracy and maximise voluntary effort. Participants completed clinical assessments (Fugl-Meyer, Action Research Arm Test) at baseline and post-intervention, as well as unassisted tracking tasks at the beginning and end of each intervention session. Data were analysed using t-tests and linear regression. From baseline to post-intervention, Fugl-Meyer scores improved, assisted and unassisted tracking performance improved, and the amount of ES required to assist tracking reduced. The concept of minimising support from ES using ILC algorithms was demonstrated. The positive results are promising with respect to reducing upper limb impairments following stroke, however, a larger study is required to confirm this.

Iterated learning and the evolution of language.

PubMed

Kirby, Simon; Griffiths, Tom; Smith, Kenny

2014-10-01

Iterated learning describes the process whereby an individual learns their behaviour by exposure to another individual's behaviour, who themselves learnt it in the same way. It can be seen as a key mechanism of cultural evolution. We review various methods for understanding how behaviour is shaped by the iterated learning process: computational agent-based simulations; mathematical modelling; and laboratory experiments in humans and non-human animals. We show how this framework has been used to explain the origins of structure in language, and argue that cultural evolution must be considered alongside biological evolution in explanations of language origins. Copyright © 2014 Elsevier Ltd. All rights reserved.

Development of a public health reporting data warehouse: lessons learned.

PubMed

Rizi, Seyed Ali Mussavi; Roudsari, Abdul

2013-01-01

Data warehouse projects are perceived to be risky and prone to failure due to many organizational and technical challenges. However, often iterative and lengthy processes of implementation of data warehouses at an enterprise level provide an opportunity for formative evaluation of these solutions. This paper describes lessons learned from successful development and implementation of the first phase of an enterprise data warehouse to support public health surveillance at British Columbia Centre for Disease Control. Iterative and prototyping approach to development, overcoming technical challenges of extraction and integration of data from large scale clinical and ancillary systems, a novel approach to record linkage, flexible and reusable modeling of clinical data, and securing senior management support at the right time were the main factors that contributed to the success of the data warehousing project.

Oxytocin attenuates trust as a subset of more general reinforcement learning, with altered reward circuit functional connectivity in males.

PubMed

Ide, Jaime S; Nedic, Sanja; Wong, Kin F; Strey, Shmuel L; Lawson, Elizabeth A; Dickerson, Bradford C; Wald, Lawrence L; La Camera, Giancarlo; Mujica-Parodi, Lilianne R

2018-07-01

Oxytocin (OT) is an endogenous neuropeptide that, while originally thought to promote trust, has more recently been found to be context-dependent. Here we extend experimental paradigms previously restricted to de novo decision-to-trust, to a more realistic environment in which social relationships evolve in response to iterative feedback over twenty interactions. In a randomized, double blind, placebo-controlled within-subject/crossover experiment of human adult males, we investigated the effects of a single dose of intranasal OT (40 IU) on Bayesian expectation updating and reinforcement learning within a social context, with associated brain circuit dynamics. Subjects participated in a neuroeconomic task (Iterative Trust Game) designed to probe iterative social learning while their brains were scanned using ultra-high field (7T) fMRI. We modeled each subject's behavior using Bayesian updating of belief-states ("willingness to trust") as well as canonical measures of reinforcement learning (learning rate, inverse temperature). Behavioral trajectories were then used as regressors within fMRI activation and connectivity analyses to identify corresponding brain network functionality affected by OT. Behaviorally, OT reduced feedback learning, without bias with respect to positive versus negative reward. Neurobiologically, reduced learning under OT was associated with muted communication between three key nodes within the reward circuit: the orbitofrontal cortex, amygdala, and lateral (limbic) habenula. Our data suggest that OT, rather than inspiring feelings of generosity, instead attenuates the brain's encoding of prediction error and therefore its ability to modulate pre-existing beliefs. This effect may underlie OT's putative role in promoting what has typically been reported as 'unjustified trust' in the face of information that suggests likely betrayal, while also resolving apparent contradictions with regard to OT's context-dependent behavioral effects. Copyright © 2018 Elsevier Inc. All rights reserved.

Indirect iterative learning control for a discrete visual servo without a camera-robot model.

PubMed

Jiang, Ping; Bamforth, Leon C A; Feng, Zuren; Baruch, John E F; Chen, YangQuan

2007-08-01

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

Iterating between lessons on concepts and procedures can improve mathematics knowledge.

PubMed

Rittle-Johnson, Bethany; Koedinger, Kenneth

2009-09-01

Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. The purpose of the current study was to evaluate the instructional benefits of an iterative lesson sequence compared to a concepts-before-procedures sequence for students learning decimal place-value concepts and arithmetic procedures. In two classroom experiments, sixth-grade students from two schools participated (N=77 and 26). Students completed six decimal lessons on an intelligent-tutoring systems. In the iterative condition, lessons cycled between concept and procedure lessons. In the concepts-first condition, all concept lessons were presented before introducing the procedure lessons. In both experiments, students in the iterative condition gained more knowledge of arithmetic procedures, including ability to transfer the procedures to problems with novel features. Knowledge of concepts was fairly comparable across conditions. Finally, pre-test knowledge of one type predicted gains in knowledge of the other type across experiments. An iterative sequencing of lessons seems to facilitate learning and transfer, particularly of mathematical procedures. The findings support an iterative perspective for the development of knowledge of concepts and procedures.

Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

PubMed

Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

2013-10-01

This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

eNOSHA, a Free, Open and Flexible Learning Object Repository--An Iterative Development Process for Global User-Friendliness

ERIC Educational Resources Information Center

Mozelius, Peter; Hettiarachchi, Enosha

2012-01-01

This paper describes the iterative development process of a Learning Object Repository (LOR), named eNOSHA. Discussions on a project for a LOR started at the e-Learning Centre (eLC) at The University of Colombo, School of Computing (UCSC) in 2007. The eLC has during the last decade been developing learning content for a nationwide e-learning…

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

A Mixed Methods Bounded Case Study: Data-Driven Decision Making within Professional Learning Communities for Response to Intervention

ERIC Educational Resources Information Center

Rodriguez, Gabriel R.

2017-01-01

A growing number of schools are implementing PLCs to address school improvement, staff engage with data to identify student needs and determine instructional interventions. This is a starting point for engaging in the iterative process of learning for the teach in order to increase student learning (Hord & Sommers, 2008). The iterative process…

Design & control of a 3D stroke rehabilitation platform.

PubMed

Cai, Z; Tong, D; Meadmore, K L; Freeman, C T; Hughes, A M; Rogers, E; Burridge, J H

2011-01-01

An upper limb stroke rehabilitation system is developed which combines electrical stimulation with mechanical arm support, to assist patients performing 3D reaching tasks in a virtual reality environment. The Stimulation Assistance through Iterative Learning (SAIL) platform applies electrical stimulation to two muscles in the arm using model-based control schemes which learn from previous trials of the task. This results in accurate movement which maximises the therapeutic effect of treatment. The principal components of the system are described and experimental results confirm its efficacy for clinical use in upper limb stroke rehabilitation. © 2011 IEEE

Using Functional Electrical Stimulation Mediated by Iterative Learning Control and Robotics to Improve Arm Movement for People With Multiple Sclerosis.

PubMed

Sampson, Patrica; Freeman, Chris; Coote, Susan; Demain, Sara; Feys, Peter; Meadmore, Katie; Hughes, Ann-Marie

2016-02-01

Few interventions address multiple sclerosis (MS) arm dysfunction but robotics and functional electrical stimulation (FES) appear promising. This paper investigates the feasibility of combining FES with passive robotic support during virtual reality (VR) training tasks to improve upper limb function in people with multiple sclerosis (pwMS). The system assists patients in following a specified trajectory path, employing an advanced model-based paradigm termed iterative learning control (ILC) to adjust the FES to improve accuracy and maximise voluntary effort. Reaching tasks were repeated six times with ILC learning the optimum control action from previous attempts. A convenience sample of five pwMS was recruited from local MS societies, and the intervention comprised 18 one-hour training sessions over 10 weeks. The accuracy of tracking performance without FES and the amount of FES delivered during training were analyzed using regression analysis. Clinical functioning of the arm was documented before and after treatment with standard tests. Statistically significant results following training included: improved accuracy of tracking performance both when assisted and unassisted by FES; reduction in maximum amount of FES needed to assist tracking; and less impairment in the proximal arm that was trained. The system was well tolerated by all participants with no increase in muscle fatigue reported. This study confirms the feasibility of FES combined with passive robot assistance as a potentially effective intervention to improve arm movement and control in pwMS and provides the basis for a follow-up study.

Machine learning in motion control

NASA Technical Reports Server (NTRS)

Su, Renjeng; Kermiche, Noureddine

1989-01-01

The existing methodologies for robot programming originate primarily from robotic applications to manufacturing, where uncertainties of the robots and their task environment may be minimized by repeated off-line modeling and identification. In space application of robots, however, a higher degree of automation is required for robot programming because of the desire of minimizing the human intervention. We discuss a new paradigm of robotic programming which is based on the concept of machine learning. The goal is to let robots practice tasks by themselves and the operational data are used to automatically improve their motion performance. The underlying mathematical problem is to solve the problem of dynamical inverse by iterative methods. One of the key questions is how to ensure the convergence of the iterative process. There have been a few small steps taken into this important approach to robot programming. We give a representative result on the convergence problem.

Iterative Design and Testing for the Development of a Game-Based Chlamydia Awareness Intervention: A Pilot Study.

PubMed

Jiang, Rui; McKanna, James; Calabrese, Samantha; Seif El-Nasr, Magy

2017-08-01

Herein we describe a methodology for developing a game-based intervention to raise awareness of Chlamydia and other sexually transmitted infections among youth in Boston's underserved communities. We engaged in three design-based experiments. These utilized mixed methods, including playtesting and assessment methods, to examine the overall effectiveness of the game. In this case, effectiveness is defined as (1) engaging the target group, (2) increasing knowledge about Chlamydia, and (3) changing attitudes toward Chlamydia testing. These three experiments were performed using participants from different communities and with slightly different versions of the game, as we iterated through the design/feedback process. Overall, participants who played the game showed a significant increase in participants' knowledge of Chlamydia compared with those in the control group (P = 0.0002). The version of the game, including elements specifically targeting systemic thinking, showed significant improvement in participants' intent to get tested compared with the version of the game without such elements (Stage 2: P > 0.05; Stage 3: P = 0.0045). Furthermore, during both Stage 2 and Stage 3, participants showed high levels of enjoyment, mood, and participation and moderate levels of game engagement and social engagement. During Stage 3, however, participants' game engagement (P = 0.0003), social engagement (P = 0.0003), and participation (P = 0.0003) were significantly higher compared with those of Stage 2. Thus, we believe that motivation improvements from Stage 2 to 3 were also effective. Finally, participants' overall learning effectiveness was correlated with their prepositive affect (r = 0.52) and their postproblem hierarchy (r = -0.54). The game improved considerably from its initial conception through three stages of iterative design and feedback. Our assessment methods for each stage targeted and integrated learning, health, and engagement outcomes. Lessons learned through this iterative design process are a great contribution to the games for health community, especially in targeting the development of health and learning goals through game design.

A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

DOE Office of Scientific and Technical Information (OSTI.GOV)

La Haye, R. J., E-mail: lahaye@fusion.gat.com

2015-12-10

ITER is an international project to design and build an experimental fusion reactor based on the “tokamak” concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of “H-mode” and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after whichmore » assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the “missing” current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM “seeding” instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a “wild card” may be broadening of the localized ECCD by the presence of the island; various theories predict broadening could occur and there is experimental evidence for broadening in DIII-D. Wider than now expected ECCD in ITER would make alignment easier to do but weaken the stabilization and thus require more rf power. In addition to updated modeling for ITER, advances in the ITER-relevant DIII-D ECCD gyrotron launch mirror control system hardware and real-time plasma control system have been made [7] and there are plans for application in DIII-D ITER demonstration discharges.« less

A fresh look at electron cyclotron current drive power requirements for stabilization of tearing modes in ITER

NASA Astrophysics Data System (ADS)

La Haye, R. J.

2015-12-01

ITER is an international project to design and build an experimental fusion reactor based on the "tokamak" concept. ITER relies upon localized electron cyclotron current drive (ECCD) at the rational safety factor q=2 to suppress or stabilize the expected poloidal mode m=2, toroidal mode n=1 neoclassical tearing mode (NTM) islands. Such islands if unmitigated degrade energy confinement, lock to the resistive wall (stop rotating), cause loss of "H-mode" and induce disruption. The International Tokamak Physics Activity (ITPA) on MHD, Disruptions and Magnetic Control joint experiment group MDC-8 on Current Drive Prevention/Stabilization of Neoclassical Tearing Modes started in 2005, after which assessments were made for the requirements for ECCD needed in ITER, particularly that of rf power and alignment on q=2 [1]. Narrow well-aligned rf current parallel to and of order of one percent of the total plasma current is needed to replace the "missing" current in the island O-points and heal or preempt (avoid destabilization by applying ECCD on q=2 in absence of the mode) the island [2-4]. This paper updates the advances in ECCD stabilization on NTMs learned in DIII-D experiments and modeling during the last 5 to 10 years as applies to stabilization by localized ECCD of tearing modes in ITER. This includes the ECCD (inside the q=1 radius) stabilization of the NTM "seeding" instability known as sawteeth (m/n=1/1) [5]. Recent measurements in DIII-D show that the ITER-similar current profile is classically unstable, curvature stabilization must not be neglected, and the small island width stabilization effect from helical ion polarization currents is stronger than was previously thought [6]. The consequences of updated assumptions in ITER modeling of the minimum well-aligned ECCD power needed are all-in-all favorable (and well-within the ITER 24 gyrotron capability) when all effects are included. However, a "wild card" may be broadening of the localized ECCD by the presence of the island; various theories predict broadening could occur and there is experimental evidence for broadening in DIII-D. Wider than now expected ECCD in ITER would make alignment easier to do but weaken the stabilization and thus require more rf power. In addition to updated modeling for ITER, advances in the ITER-relevant DIII-D ECCD gyrotron launch mirror control system hardware and real-time plasma control system have been made [7] and there are plans for application in DIII-D ITER demonstration discharges.

Increasing High School Student Interest in Science: An Action Research Study

NASA Astrophysics Data System (ADS)

Vartuli, Cindy A.

An action research study was conducted to determine how to increase student interest in learning science and pursuing a STEM career. The study began by exploring 10th-grade student and teacher perceptions of student interest in science in order to design an instructional strategy for stimulating student interest in learning and pursuing science. Data for this study included responses from 270 students to an on-line science survey and interviews with 11 students and eight science teachers. The action research intervention included two iterations of the STEM Career Project. The first iteration introduced four chemistry classes to the intervention. The researcher used student reflections and a post-project survey to determine if the intervention had influence on the students' interest in pursuing science. The second iteration was completed by three science teachers who had implemented the intervention with their chemistry classes, using student reflections and post-project surveys, as a way to make further procedural refinements and improvements to the intervention and measures. Findings from the exploratory phase of the study suggested students generally had interest in learning science but increasing that interest required including personally relevant applications and laboratory experiences. The intervention included a student-directed learning module in which students investigated three STEM careers and presented information on one of their chosen careers. The STEM Career Project enabled students to explore career possibilities in order to increase their awareness of STEM careers. Findings from the first iteration of the intervention suggested a positive influence on student interest in learning and pursuing science. The second iteration included modifications to the intervention resulting in support for the findings of the first iteration. Results of the second iteration provided modifications that would allow the project to be used for different academic levels. Insights from conducting the action research study provided the researcher with effective ways to make positive changes in her own teaching praxis and the tools used to improve student awareness of STEM career options.

Off-Policy Actor-Critic Structure for Optimal Control of Unknown Systems With Disturbances.

PubMed

Song, Ruizhuo; Lewis, Frank L; Wei, Qinglai; Zhang, Huaguang

2016-05-01

An optimal control method is developed for unknown continuous-time systems with unknown disturbances in this paper. The integral reinforcement learning (IRL) algorithm is presented to obtain the iterative control. Off-policy learning is used to allow the dynamics to be completely unknown. Neural networks are used to construct critic and action networks. It is shown that if there are unknown disturbances, off-policy IRL may not converge or may be biased. For reducing the influence of unknown disturbances, a disturbances compensation controller is added. It is proven that the weight errors are uniformly ultimately bounded based on Lyapunov techniques. Convergence of the Hamiltonian function is also proven. The simulation study demonstrates the effectiveness of the proposed optimal control method for unknown systems with disturbances.

Young children make their gestural communication systems more language-like: segmentation and linearization of semantic elements in motion events.

PubMed

Clay, Zanna; Pople, Sally; Hood, Bruce; Kita, Sotaro

2014-08-01

Research on Nicaraguan Sign Language, created by deaf children, has suggested that young children use gestures to segment the semantic elements of events and linearize them in ways similar to those used in signed and spoken languages. However, it is unclear whether this is due to children's learning processes or to a more general effect of iterative learning. We investigated whether typically developing children, without iterative learning, segment and linearize information. Gestures produced in the absence of speech to express a motion event were examined in 4-year-olds, 12-year-olds, and adults (all native English speakers). We compared the proportions of gestural expressions that segmented semantic elements into linear sequences and that encoded them simultaneously. Compared with adolescents and adults, children reshaped the holistic stimuli by segmenting and recombining their semantic features into linearized sequences. A control task on recognition memory ruled out the possibility that this was due to different event perception or memory. Young children spontaneously bring fundamental properties of language into their communication system. © The Author(s) 2014.

Composition of web services using Markov decision processes and dynamic programming.

PubMed

Uc-Cetina, Víctor; Moo-Mena, Francisco; Hernandez-Ucan, Rafael

2015-01-01

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity.

An Iterative Learning Control Approach to Improving Fidelity in Internet-Distributed Hardware-in-the-Loop Simulation

DTIC Science & Technology

2012-06-15

pp. 535-543. [17] Compere , M., Goodell, J., Simon, M., Smith, W., and Brudnak, M., 2006, "Robust Control Techniques Enabling Duty Cycle...Technical Paper, 2006-01-3077. [18] Goodell, J., Compere , M., Simon, M., Smith, W., Wright, R., and Brudnak, M., 2006, "Robust Control Techniques for...Smith, W., Compere , M., Goodell, J., Holtz, D., Mortsfield, T., and Shvartsman, A., 2007, "Soldier/Harware-in-the-Loop Simulation- Based Combat Vehicle

Ensuring Success of Adaptive Control Research Through Project Lifecycle Risk Mitigation

NASA Technical Reports Server (NTRS)

Pavlock, Kate M.

2011-01-01

Lessons Learne: 1. Design-out unnecessary risk to prevent excessive mitigation management during flight. 2. Consider iterative checkouts to confirm or improve human factor characteristics. 3. Consider the total flight test profile to uncover unanticipated human-algorithm interactions. 4. Consider test card cadence as a metric to assess test readiness. 5. Full-scale flight test is critical to development, maturation, and acceptance of adaptive control laws for operational use.

Fast calculation of the `ILC norm' in iterative learning control

NASA Astrophysics Data System (ADS)

Rice, Justin K.; van Wingerden, Jan-Willem

2013-06-01

In this paper, we discuss and demonstrate a method for the exploitation of matrix structure in computations for iterative learning control (ILC). In Barton, Bristow, and Alleyne [International Journal of Control, 83(2), 1-8 (2010)], a special insight into the structure of the lifted convolution matrices involved in ILC is used along with a modified Lanczos method to achieve very fast computational bounds on the learning convergence, by calculating the 'ILC norm' in ? computational complexity. In this paper, we show how their method is equivalent to a special instance of the sequentially semi-separable (SSS) matrix arithmetic, and thus can be extended to many other computations in ILC, and specialised in some cases to even faster methods. Our SSS-based methodology will be demonstrated on two examples: a linear time-varying example resulting in the same ? complexity as in Barton et al., and a linear time-invariant example where our approach reduces the computational complexity to ?, thus decreasing the computation time, for an example, from the literature by a factor of almost 100. This improvement is achieved by transforming the norm computation via a linear matrix inequality into a check of positive definiteness - which allows us to further exploit the almost-Toeplitz properties of the matrix, and additionally provides explicit upper and lower bounds on the norm of the matrix, instead of the indirect Ritz estimate. These methods are now implemented in a MATLAB toolbox, freely available on the Internet.

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

Not All Wizards Are from Oz: Iterative Design of Intelligent Learning Environments by Communication Capacity Tapering

ERIC Educational Resources Information Center

Mavrikis, Manolis; Gutierrez-Santos, Sergio

2010-01-01

This paper presents a methodology for the design of intelligent learning environments. We recognise that in the educational technology field, theory development and system-design should be integrated and rely on an iterative process that addresses: (a) the difficulty to elicit precise, concise, and operationalized knowledge from "experts" and (b)…

Foucauldian Iterative Learning Conversations--An Example of Organisational Change: Developing Conjoint-Work between EPS and Social Workers

ERIC Educational Resources Information Center

Apter, Brian

2014-01-01

An organisational change-process in a UK local authority (LA) over two years is examined using transcribed excerpts from three meetings. The change-process is analysed using a Foucauldian analytical tool--Iterative Learning Conversations (ILCS). An Educational Psychology Service was changed from being primarily an education-focussed…

Learning Efficient Sparse and Low Rank Models.

PubMed

Sprechmann, P; Bronstein, A M; Sapiro, G

2015-09-01

Parsimony, including sparsity and low rank, has been shown to successfully model data in numerous machine learning and signal processing tasks. Traditionally, such modeling approaches rely on an iterative algorithm that minimizes an objective function with parsimony-promoting terms. The inherently sequential structure and data-dependent complexity and latency of iterative optimization constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Another limitation encountered by these modeling techniques is the difficulty of their inclusion in discriminative learning scenarios. In this work, we propose to move the emphasis from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling, in which a learned deterministic fixed-complexity pursuit process is used in lieu of iterative optimization. We show a principled way to construct learnable pursuit process architectures for structured sparse and robust low rank models, derived from the iteration of proximal descent algorithms. These architectures learn to approximate the exact parsimonious representation at a fraction of the complexity of the standard optimization methods. We also show that appropriate training regimes allow to naturally extend parsimonious models to discriminative settings. State-of-the-art results are demonstrated on several challenging problems in image and audio processing with several orders of magnitude speed-up compared to the exact optimization algorithms.

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Quantum-Inspired Multidirectional Associative Memory With a Self-Convergent Iterative Learning.

PubMed

Masuyama, Naoki; Loo, Chu Kiong; Seera, Manjeevan; Kubota, Naoyuki

2018-04-01

Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.

Finding the Optimal Guidance for Enhancing Anchored Instruction

ERIC Educational Resources Information Center

Zydney, Janet Mannheimer; Bathke, Arne; Hasselbring, Ted S.

2014-01-01

This study investigated the effect of different methods of guidance with anchored instruction on students' mathematical problem-solving performance. The purpose of this research was to iteratively design a learning environment to find the optimal level of guidance. Two iterations of the software were compared. The first iteration used explicit…

Composition of Web Services Using Markov Decision Processes and Dynamic Programming

PubMed Central

Uc-Cetina, Víctor; Moo-Mena, Francisco; Hernandez-Ucan, Rafael

2015-01-01

We propose a Markov decision process model for solving the Web service composition (WSC) problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate our approach, with artificial and real data. The experimental results show the reliability of the model and the methods employed, with policy iteration being the best one in terms of the minimum number of iterations needed to estimate an optimal policy, with the highest Quality of Service attributes. Our experimental work shows how the solution of a WSC problem involving a set of 100,000 individual Web services and where a valid composition requiring the selection of 1,000 services from the available set can be computed in the worst case in less than 200 seconds, using an Intel Core i5 computer with 6 GB RAM. Moreover, a real WSC problem involving only 7 individual Web services requires less than 0.08 seconds, using the same computational power. Finally, a comparison with two popular reinforcement learning algorithms, sarsa and Q-learning, shows that these algorithms require one or two orders of magnitude and more time than policy iteration, iterative policy evaluation, and value iteration to handle WSC problems of the same complexity. PMID:25874247

Developing Conceptual Understanding and Procedural Skill in Mathematics: An Iterative Process.

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Siegler, Robert S.; Alibali, Martha Wagner

2001-01-01

Proposes that conceptual and procedural knowledge develop in an iterative fashion and improved problem representation is one mechanism underlying the relations between them. Two experiments were conducted with 5th and 6th grade students learning about decimal fractions. Results indicate conceptual and procedural knowledge do develop, iteratively,…

Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Hanquan

2016-03-01

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

Learning to improve iterative repair scheduling

NASA Technical Reports Server (NTRS)

Zweben, Monte; Davis, Eugene

1992-01-01

This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone.

Physics and Control of Locked Modes in the DIII-D Tokamak

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volpe, Francesco

This Final Technical Report summarizes an investigation, carried out under the auspices of the DOE Early Career Award, of the physics and control of non-rotating magnetic islands (“locked modes”) in tokamak plasmas. Locked modes are one of the main causes of disruptions in present tokamaks, and could be an even bigger concern in ITER, due to its relatively high beta (favoring the formation of Neoclassical Tearing Mode islands) and low rotation (favoring locking). For these reasons, this research had the goal of studying and learning how to control locked modes in the DIII-D National Fusion Facility under ITER-relevant conditions ofmore » high pressure and low rotation. Major results included: the first full suppression of locked modes and avoidance of the associated disruptions; the demonstration of error field detection from the interaction between locked modes, applied rotating fields and intrinsic errors; the analysis of a vast database of disruptive locked modes, which led to criteria for disruption prediction and avoidance.« less

Iterating between Lessons on Concepts and Procedures Can Improve Mathematics Knowledge

ERIC Educational Resources Information Center

Rittle-Johnson, Bethany; Koedinger, Kenneth

2009-01-01

Background: Knowledge of concepts and procedures seems to develop in an iterative fashion, with increases in one type of knowledge leading to increases in the other type of knowledge. This suggests that iterating between lessons on concepts and procedures may improve learning. Aims: The purpose of the current study was to evaluate the…

Solving ill-posed inverse problems using iterative deep neural networks

NASA Astrophysics Data System (ADS)

Adler, Jonas; Öktem, Ozan

2017-12-01

We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the ‘gradient’ component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 × 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).

Filtering sensory information with XCSF: improving learning robustness and robot arm control performance.

PubMed

Kneissler, Jan; Stalph, Patrick O; Drugowitsch, Jan; Butz, Martin V

2014-01-01

It has been shown previously that the control of a robot arm can be efficiently learned using the XCSF learning classifier system, which is a nonlinear regression system based on evolutionary computation. So far, however, the predictive knowledge about how actual motor activity changes the state of the arm system has not been exploited. In this paper, we utilize the forward velocity kinematics knowledge of XCSF to alleviate the negative effect of noisy sensors for successful learning and control. We incorporate Kalman filtering for estimating successive arm positions, iteratively combining sensory readings with XCSF-based predictions of hand position changes over time. The filtered arm position is used to improve both trajectory planning and further learning of the forward velocity kinematics. We test the approach on a simulated kinematic robot arm model. The results show that the combination can improve learning and control performance significantly. However, it also shows that variance estimates of XCSF prediction may be underestimated, in which case self-delusional spiraling effects can hinder effective learning. Thus, we introduce a heuristic parameter, which can be motivated by theory, and which limits the influence of XCSF's predictions on its own further learning input. As a result, we obtain drastic improvements in noise tolerance, allowing the system to cope with more than 10 times higher noise levels.

Photoacoustic image reconstruction via deep learning

NASA Astrophysics Data System (ADS)

Antholzer, Stephan; Haltmeier, Markus; Nuster, Robert; Schwab, Johannes

2018-02-01

Applying standard algorithms to sparse data problems in photoacoustic tomography (PAT) yields low-quality images containing severe under-sampling artifacts. To some extent, these artifacts can be reduced by iterative image reconstruction algorithms which allow to include prior knowledge such as smoothness, total variation (TV) or sparsity constraints. These algorithms tend to be time consuming as the forward and adjoint problems have to be solved repeatedly. Further, iterative algorithms have additional drawbacks. For example, the reconstruction quality strongly depends on a-priori model assumptions about the objects to be recovered, which are often not strictly satisfied in practical applications. To overcome these issues, in this paper, we develop direct and efficient reconstruction algorithms based on deep learning. As opposed to iterative algorithms, we apply a convolutional neural network, whose parameters are trained before the reconstruction process based on a set of training data. For actual image reconstruction, a single evaluation of the trained network yields the desired result. Our presented numerical results (using two different network architectures) demonstrate that the proposed deep learning approach reconstructs images with a quality comparable to state of the art iterative reconstruction methods.

The Iterated Classification Game: A New Model of the Cultural Transmission of Language

PubMed Central

Swarup, Samarth; Gasser, Les

2010-01-01

The Iterated Classification Game (ICG) combines the Classification Game with the Iterated Learning Model (ILM) to create a more realistic model of the cultural transmission of language through generations. It includes both learning from parents and learning from peers. Further, it eliminates some of the chief criticisms of the ILM: that it does not study grounded languages, that it does not include peer learning, and that it builds in a bias for compositional languages. We show that, over the span of a few generations, a stable linguistic system emerges that can be acquired very quickly by each generation, is compositional, and helps the agents to solve the classification problem with which they are faced. The ICG also leads to a different interpretation of the language acquisition process. It suggests that the role of parents is to initialize the linguistic system of the child in such a way that subsequent interaction with peers results in rapid convergence to the correct language. PMID:20190877

Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning optimal control approach.

PubMed

Liu, Derong; Wang, Ding; Li, Hongliang

2014-02-01

In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme.

Quickprop method to speed up learning process of Artificial Neural Network in money's nominal value recognition case

NASA Astrophysics Data System (ADS)

Swastika, Windra

2017-03-01

A money's nominal value recognition system has been developed using Artificial Neural Network (ANN). ANN with Back Propagation has one disadvantage. The learning process is very slow (or never reach the target) in the case of large number of iteration, weight and samples. One way to speed up the learning process is using Quickprop method. Quickprop method is based on Newton's method and able to speed up the learning process by assuming that the weight adjustment (E) is a parabolic function. The goal is to minimize the error gradient (E'). In our system, we use 5 types of money's nominal value, i.e. 1,000 IDR, 2,000 IDR, 5,000 IDR, 10,000 IDR and 50,000 IDR. One of the surface of each nominal were scanned and digitally processed. There are 40 patterns to be used as training set in ANN system. The effectiveness of Quickprop method in the ANN system was validated by 2 factors, (1) number of iterations required to reach error below 0.1; and (2) the accuracy to predict nominal values based on the input. Our results shows that the use of Quickprop method is successfully reduce the learning process compared to Back Propagation method. For 40 input patterns, Quickprop method successfully reached error below 0.1 for only 20 iterations, while Back Propagation method required 2000 iterations. The prediction accuracy for both method is higher than 90%.

Reinforcement Learning with Autonomous Small Unmanned Aerial Vehicles in Cluttered Environments

NASA Technical Reports Server (NTRS)

Tran, Loc; Cross, Charles; Montague, Gilbert; Motter, Mark; Neilan, James; Qualls, Garry; Rothhaar, Paul; Trujillo, Anna; Allen, B. Danette

2015-01-01

We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.

Active machine learning-driven experimentation to determine compound effects on protein patterns.

PubMed

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Deep learning methods to guide CT image reconstruction and reduce metal artifacts

NASA Astrophysics Data System (ADS)

Gjesteby, Lars; Yang, Qingsong; Xi, Yan; Zhou, Ye; Zhang, Junping; Wang, Ge

2017-03-01

The rapidly-rising field of machine learning, including deep learning, has inspired applications across many disciplines. In medical imaging, deep learning has been primarily used for image processing and analysis. In this paper, we integrate a convolutional neural network (CNN) into the computed tomography (CT) image reconstruction process. Our first task is to monitor the quality of CT images during iterative reconstruction and decide when to stop the process according to an intelligent numerical observer instead of using a traditional stopping rule, such as a fixed error threshold or a maximum number of iterations. After training on ground truth images, the CNN was successful in guiding an iterative reconstruction process to yield high-quality images. Our second task is to improve a sinogram to correct for artifacts caused by metal objects. A large number of interpolation and normalization-based schemes were introduced for metal artifact reduction (MAR) over the past four decades. The NMAR algorithm is considered a state-of-the-art method, although residual errors often remain in the reconstructed images, especially in cases of multiple metal objects. Here we merge NMAR with deep learning in the projection domain to achieve additional correction in critical image regions. Our results indicate that deep learning can be a viable tool to address CT reconstruction challenges.

Reinforcement learning produces dominant strategies for the Iterated Prisoner's Dilemma.

PubMed

Harper, Marc; Knight, Vincent; Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner's Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.

Novel aspects of plasma control in ITER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humphreys, D.; Jackson, G.; Walker, M.

2015-02-15

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g., current profile regulation, tearing mode (TM) suppression), control mathematics (e.g., algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g., methods for management of highly subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

Novel aspects of plasma control in ITER

DOE PAGES

Humphreys, David; Ambrosino, G.; de Vries, Peter; ...

2015-02-12

ITER plasma control design solutions and performance requirements are strongly driven by its nuclear mission, aggressive commissioning constraints, and limited number of operational discharges. In addition, high plasma energy content, heat fluxes, neutron fluxes, and very long pulse operation place novel demands on control performance in many areas ranging from plasma boundary and divertor regulation to plasma kinetics and stability control. Both commissioning and experimental operations schedules provide limited time for tuning of control algorithms relative to operating devices. Although many aspects of the control solutions required by ITER have been well-demonstrated in present devices and even designed satisfactorily formore » ITER application, many elements unique to ITER including various crucial integration issues are presently under development. We describe selected novel aspects of plasma control in ITER, identifying unique parts of the control problem and highlighting some key areas of research remaining. Novel control areas described include control physics understanding (e.g. current profile regulation, tearing mode suppression (TM)), control mathematics (e.g. algorithmic and simulation approaches to high confidence robust performance), and integration solutions (e.g. methods for management of highly-subscribed control resources). We identify unique aspects of the ITER TM suppression scheme, which will pulse gyrotrons to drive current within a magnetic island, and turn the drive off following suppression in order to minimize use of auxiliary power and maximize fusion gain. The potential role of active current profile control and approaches to design in ITER are discussed. Finally, issues and approaches to fault handling algorithms are described, along with novel aspects of actuator sharing in ITER.« less

Multi Agent Systems with Symbiotic Learning and Evolution using GNP

NASA Astrophysics Data System (ADS)

Eguchi, Toru; Hirasawa, Kotaro; Hu, Jinglu; Murata, Junichi

Recently, various attempts relevant to Multi Agent Systems (MAS) which is one of the most promising systems based on Distributed Artificial Intelligence have been studied to control large and complicated systems efficiently. In these trends of MAS, Multi Agent Systems with Symbiotic Learning and Evolution named Masbiole has been proposed. In Masbiole, symbiotic phenomena among creatures are considered in the process of learning and evolution of MAS. So we can expect more flexible and sophisticated solutions than conventional MAS. In this paper, we apply Masbiole to Iterative Prisoner’s Dilemma Games (IPD Games) using Genetic Network Programming (GNP) which is a newly developed evolutionary computation method for constituting agents. Some characteristics of Masbiole using GNP in IPD Games are clarified.

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

PubMed Central

Liu, Bo; Chen, Sanfeng; Li, Shuai; Liang, Yongsheng

2012-01-01

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces. PMID:22736969

The impact of iterated games on traffic flow at noncontrolled intersections

NASA Astrophysics Data System (ADS)

Zhao, Chao; Jia, Ning

2015-05-01

Intersections without signal control widely exist in urban road networks. This paper studied the traffic flow in a noncontrolled intersection within an iterated game framework. We assume drivers have learning ability and can repetitively adjust their strategies (to give way or to rush through) in the intersection according to memories. A cellular automata model is applied to investigate the characteristics of the traffic flow. Numerical experiments indicate two main findings. First, the traffic flow experiences a "volcano-shaped" fundamental diagram with three different phases. Second, most drivers choose to give way in the intersection, but the aggressive drivers cannot be completely eliminated, which is coincident with field observations. Analysis are also given out to explain the observed phenomena. These findings allow deeper insight of the real-world bottleneck traffic flow.

An analysis of value function learning with piecewise linear control

NASA Astrophysics Data System (ADS)

Tutsoy, Onder; Brown, Martin

2016-05-01

Reinforcement learning (RL) algorithms attempt to learn optimal control actions by iteratively estimating a long-term measure of system performance, the so-called value function. For example, RL algorithms have been applied to walking robots to examine the connection between robot motion and the brain, which is known as embodied cognition. In this paper, RL algorithms are analysed using an exemplar test problem. A closed form solution for the value function is calculated and this is represented in terms of a set of basis functions and parameters, which is used to investigate parameter convergence. The value function expression is shown to have a polynomial form where the polynomial terms depend on the plant's parameters and the value function's discount factor. It is shown that the temporal difference error introduces a null space for the differenced higher order basis associated with the effects of controller switching (saturated to linear control or terminating an experiment) apart from the time of the switch. This leads to slow convergence in the relevant subspace. It is also shown that badly conditioned learning problems can occur, and this is a function of the value function discount factor and the controller switching points. Finally, a comparison is performed between the residual gradient and TD(0) learning algorithms, and it is shown that the former has a faster rate of convergence for this test problem.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Community Based Learning and Civic Engagement: Informal Learning among Adult Volunteers in Community Organizations

ERIC Educational Resources Information Center

Mundel, Karsten; Schugurensky, Daniel

2008-01-01

Many iterations of community based learning employ models, such as consciousness raising groups, cultural circles, and participatory action research. In all of them, learning is a deliberate part of an explicit educational activity. This article explores another realm of community learning: the informal learning that results from volunteering in…

Reinforcement learning produces dominant strategies for the Iterated Prisoner’s Dilemma

PubMed Central

Jones, Martin; Koutsovoulos, Georgios; Glynatsi, Nikoleta E.; Campbell, Owen

2017-01-01

We present tournament results and several powerful strategies for the Iterated Prisoner’s Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also. PMID:29228001

Applying the scientific method to small catchment studies: Areview of the Panola Mountain experience

USGS Publications Warehouse

Hooper, R.P.

2001-01-01

A hallmark of the scientific method is its iterative application to a problem to increase and refine the understanding of the underlying processes controlling it. A successful iterative application of the scientific method to catchment science (including the fields of hillslope hydrology and biogeochemistry) has been hindered by two factors. First, the scale at which controlled experiments can be performed is much smaller than the scale of the phenomenon of interest. Second, computer simulation models generally have not been used as hypothesis-testing tools as rigorously as they might have been. Model evaluation often has gone only so far as evaluation of goodness of fit, rather than a full structural analysis, which is more useful when treating the model as a hypothesis. An iterative application of a simple mixing model to the Panola Mountain Research Watershed is reviewed to illustrate the increase in understanding gained by this approach and to discern general principles that may be applicable to other studies. The lessons learned include the need for an explicitly stated conceptual model of the catchment, the definition of objective measures of its applicability, and a clear linkage between the scale of observations and the scale of predictions. Published in 2001 by John Wiley & Sons. Ltd.

Technology Education Using a Novel Approach in e-Learning-Towards Optimizing the Quality of Learning Outcomes

NASA Astrophysics Data System (ADS)

Malkawi, M. I.; Hawarey, M. M.

2012-04-01

Ever since the advent of the new era in presenting taught material in Electronic Form, international bodies, academic institutions, public sectors, as well as specialized entities in the private sector, globally, have all persevered to exploit the power of Distance Learning and e-Learning to disseminate the knowledge in Science and Art using the ubiquitous World Wide Web and its supporting Internet and Internetworking. Many Science & Education-sponsoring bodies, like UNESCO, the European Community, and the World Bank have been keen at funding multinational Distance Learning projects, many of which were directed at an educated audience in certain technical areas. Many countries around the Middle East have found a number of interested European partners to launch funding requests, and were generally successful in their solicitation efforts for the needed funds from these funding bodies. Albeit their intricacies in generating a wealth of knowledge in electronic form, many of the e-Learning schemas developed thus far, have only pursued their goals in the most conventional of ways; In essence, there had been little innovation introduced to gain anything, if any, above traditional classroom lecturing, other than, of course, the gained advantage of the simultaneous online testing and evaluation of the learned material by the examinees. In a sincere effort to change the way in which people look at the merits of e-Learning, and seek the most out of it, we shall propose a novel approach aimed at optimizing the learning outcomes of presented materials. In this paper we propose what shall henceforth be called as Iterative e-Learning. In Iterative e-Learning, as the name implies, a student uses some form of electronic media to access course material in a specific subject. At the end of each phase (Section, Chapter, Session, etc.) on a specific topic, the student is assessed online of how much he/she would have achieved before he/she would move on. If the student fails, due to some delinquency on a particular topic, the online process of e-Learning would take the student at some more detailed and deeper level on the subject matter where he/she had failed; once the student bridges the gap, to this end, then the ongoing e-Learning process would carry him/her further up the next level of the subject matter he/she is pursuing. This process is carried on at all levels of learning: section, chapter, and course level. A student may not progress to the next course level before he/she would pass the entire course at 80% or more. If in the process of repeating some section, chapter, or a whole course, then the student shall be required to score a higher percentage than the mere 80% he was required to attain the first time around; say 5% more per iteration he/she makes. Here, students going through Iterative e-Learning shall be allowed to move on to the next level of learning sooner than others if the time that takes them to learn a particular topic is shorter than would normally require an average student to expend, provided, of course, they make it through all the required assessment phases. Unlike the traditional ways of classroom or online lecturing, a student going through Iterative e-Learning is expected to achieve a quality of learning never before achieved via standard pedagogical methodologies. With Iterative e-Learning, it is expected that poorly accredited academic institutions will be able, for the first time, to produce the quality of graduates who are more capable of competing for highly paying jobs globally, and to be of the quality of contributing in more industry-supported economies.

Analysis and Design of a Decision Support System for Silas B. Hays Army Community Hospital

DTIC Science & Technology

1988-09-01

develop the DSS. This collaboration allows the user to learn about the power decision support can give to the decision maker and... projects under their control. A DSS is developed to provide decision support for a specific manager or group , and con- sequently falls under the ... It is possible the first iteration could be developed in more than one programming language and results compared . Once the first

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Remix as Professional Learning: Educators' Iterative Literacy Practice in CLMOOC

ERIC Educational Resources Information Center

Smith, Anna; West-Puckett, Stephanie; Cantrill, Christina; Zamora, Mia

2016-01-01

The Connected Learning Massive Open Online Collaboration (CLMOOC) is an online professional development experience designed as an openly networked, production-centered, participatory learning collaboration for educators. Addressing the paucity of research that investigates learning processes in MOOC experiences, this paper examines the situated…

Distance Metric Learning via Iterated Support Vector Machines.

PubMed

Zuo, Wangmeng; Wang, Faqiang; Zhang, David; Lin, Liang; Huang, Yuchi; Meng, Deyu; Zhang, Lei

2017-07-11

Distance metric learning aims to learn from the given training data a valid distance metric, with which the similarity between data samples can be more effectively evaluated for classification. Metric learning is often formulated as a convex or nonconvex optimization problem, while most existing methods are based on customized optimizers and become inefficient for large scale problems. In this paper, we formulate metric learning as a kernel classification problem with the positive semi-definite constraint, and solve it by iterated training of support vector machines (SVMs). The new formulation is easy to implement and efficient in training with the off-the-shelf SVM solvers. Two novel metric learning models, namely Positive-semidefinite Constrained Metric Learning (PCML) and Nonnegative-coefficient Constrained Metric Learning (NCML), are developed. Both PCML and NCML can guarantee the global optimality of their solutions. Experiments are conducted on general classification, face verification and person re-identification to evaluate our methods. Compared with the state-of-the-art approaches, our methods can achieve comparable classification accuracy and are efficient in training.

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

New Standards Put the Spotlight on Professional Learning

ERIC Educational Resources Information Center

Mizell, Hayes; Hord, Shirley; Killion, Joellen; Hirsh, Stephanie

2011-01-01

Learning Forward introduces new Standards for Professional Learning. This is the third iteration of standards outlining the characteristics of professional learning that lead to effective teaching practices, supportive leadership, and improved student results. The standards are not a prescription for how education leaders and public officials…

Theorising Teaching and Learning: Pre-Service Teachers' Theoretical Awareness of Learning

ERIC Educational Resources Information Center

Brante, Göran; Holmqvist Olander, Mona; Holmquist, Per-Ola; Palla, Marta

2015-01-01

We examine pre-service teachers' theoretical learning during one five-week training module, and their educators' learning about better lecture design to foster student learning. The study is iterative: interventions (one per group) were implemented sequentially in student groups A-C, the results of the previous intervention serving as the baseline…

Learning to Teach Elementary Science Through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

NASA Astrophysics Data System (ADS)

Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

2015-12-01

Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves together cycles of enactment, core practices in science education and culturally relevant pedagogies. The theoretical foundation draws upon situated learning theory and communities of practice. Using video analysis by PSTs and course artifacts, the authors studied how the iterative process of these cycles guided PSTs development as teachers of elementary science. Findings demonstrate how PSTs were drawing on resources to inform practice, purposefully noticing their practice, renegotiating their roles in teaching, and reconsidering "professional blindness" through cultural practice.

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter C.

1990-01-01

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study.

PubMed

Tziraki, Chariklia; Berenbaum, Rakel; Gross, Daniel; Abikhzer, Judith; Ben-David, Boaz M

2017-07-31

The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes-all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Our study demonstrated that PwD's speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD's abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. ©Chariklia Tziraki, Rakel Berenbaum, Daniel Gross, Judith Abikhzer, Boaz M Ben-David. Originally published in JMIR Serious Games (http://games.jmir.org), 31.07.2017.

Designing Serious Computer Games for People With Moderate and Advanced Dementia: Interdisciplinary Theory-Driven Pilot Study

PubMed Central

Gross, Daniel; Abikhzer, Judith

2017-01-01

Background The field of serious games for people with dementia (PwD) is mostly driven by game-design principals typically applied to games created by and for younger individuals. Little has been done developing serious games to help PwD maintain cognition and to support functionality. Objectives We aimed to create a theory-based serious game for PwD, with input from a multi-disciplinary team familiar with aging, dementia, and gaming theory, as well as direct input from end users (the iterative process). Targeting enhanced self-efficacy in daily activities, the goal was to generate a game that is acceptable, accessible and engaging for PwD. Methods The theory-driven game development was based on the following learning theories: learning in context, errorless learning, building on capacities, and acknowledging biological changes—all with the aim to boost self-efficacy. The iterative participatory process was used for game screen development with input of 34 PwD and 14 healthy community dwelling older adults, aged over 65 years. Development of game screens was informed by the bio-psychological aging related disabilities (ie, motor, visual, and perception) as well as remaining neuropsychological capacities (ie, implicit memory) of PwD. At the conclusion of the iterative development process, a prototype game with 39 screens was used for a pilot study with 24 PwD and 14 healthy community dwelling older adults. The game was played twice weekly for 10 weeks. Results Quantitative analysis showed that the average speed of successful screen completion was significantly longer for PwD compared with healthy older adults. Both PwD and controls showed an equivalent linear increase in the speed for task completion with practice by the third session (P<.02). Most important, the rate of improved processing speed with practice was not statistically different between PwD and controls. This may imply that some form of learning occurred for PwD at a nonsignificantly different rate than for controls. Qualitative results indicate that PwD found the game engaging and fun. Healthy older adults found the game too easy. Increase in self-reported self-efficacy was documented with PwD only. Conclusions Our study demonstrated that PwD’s speed improved with practice at the same rate as healthy older adults. This implies that when tasks are designed to match PwD’s abilities, learning ensues. In addition, this pilot study of a serious game, designed for PwD, was accessible, acceptable, and enjoyable for end users. Games designed based on learning theories and input of end users and a multi-disciplinary team familiar with dementia and aging may have the potential of maintaining capacity and improving functionality of PwD. A larger longer study is needed to confirm our findings and evaluate the use of these games in assessing cognitive status and functionality. PMID:28760730

Active machine learning-driven experimentation to determine compound effects on protein patterns

PubMed Central

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-01-01

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance. DOI: http://dx.doi.org/10.7554/eLife.10047.001 PMID:26840049

Iterative free-energy optimization for recurrent neural networks (INFERNO).

PubMed

Pitti, Alexandre; Gaussier, Philippe; Quoy, Mathias

2017-01-01

The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes' synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

Iterative inversion of deformation vector fields with feedback control.

PubMed

Dubey, Abhishek; Iliopoulos, Alexandros-Stavros; Sun, Xiaobai; Yin, Fang-Fang; Ren, Lei

2018-05-14

Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. Our analysis captures properties of DVF data associated with clinical CT images, and provides new understanding of iterative DVF inversion algorithms with a simple residual feedback control. Adaptive control is necessary and highly effective in the presence of nonsmall NTDCs. The adaptive iterations or the spectral measures, or both, may potentially be incorporated into deformable image registration methods. © 2018 American Association of Physicists in Medicine.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

Integrating Concept Mapping into Information Systems Education for Meaningful Learning and Assessment

ERIC Educational Resources Information Center

Wei, Wei; Yue, Kwok-Bun

2017-01-01

Concept map (CM) is a theoretically sound yet easy to learn tool and can be effectively used to represent knowledge. Even though many disciplines have adopted CM as a teaching and learning tool to improve learning effectiveness, its application in IS curriculum is sparse. Meaningful learning happens when one iteratively integrates new concepts and…

Computer-Supported Team-Based Learning: The Impact of Motivation, Enjoyment and Team Contributions on Learning Outcomes

ERIC Educational Resources Information Center

Gomez, Elizabeth Avery; Wu, Dezhi; Passerini, Katia

2010-01-01

The benefits of teamwork and collaboration have long been advocated by many educational theories, such as constructivist and social learning models. Among the various applications of collaborative learning, the iterative team-based learning (TBL) process proposed by Michaelsen, Fink, and Knight (2002) has been successfully used in the classroom…

A generalised optimal linear quadratic tracker with universal applications. Part 2: discrete-time systems

NASA Astrophysics Data System (ADS)

Ebrahimzadeh, Faezeh; Tsai, Jason Sheng-Hong; Chung, Min-Ching; Liao, Ying Ting; Guo, Shu-Mei; Shieh, Leang-San; Wang, Li

2017-01-01

Contrastive to Part 1, Part 2 presents a generalised optimal linear quadratic digital tracker (LQDT) with universal applications for the discrete-time (DT) systems. This includes (1) a generalised optimal LQDT design for the system with the pre-specified trajectories of the output and the control input and additionally with both the input-to-output direct-feedthrough term and known/estimated system disturbances or extra input/output signals; (2) a new optimal filter-shaped proportional plus integral state-feedback LQDT design for non-square non-minimum phase DT systems to achieve a minimum-phase-like tracking performance; (3) a new approach for computing the control zeros of the given non-square DT systems; and (4) a one-learning-epoch input-constrained iterative learning LQDT design for the repetitive DT systems.

Automated Knowledge Discovery From Simulators

NASA Technical Reports Server (NTRS)

Burl, Michael; DeCoste, Dennis; Mazzoni, Dominic; Scharenbroich, Lucas; Enke, Brian; Merline, William

2007-01-01

A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.

Progress in Development of the ITER Plasma Control System Simulation Platform

NASA Astrophysics Data System (ADS)

Walker, Michael; Humphreys, David; Sammuli, Brian; Ambrosino, Giuseppe; de Tommasi, Gianmaria; Mattei, Massimiliano; Raupp, Gerhard; Treutterer, Wolfgang; Winter, Axel

2017-10-01

We report on progress made and expected uses of the Plasma Control System Simulation Platform (PCSSP), the primary test environment for development of the ITER Plasma Control System (PCS). PCSSP will be used for verification and validation of the ITER PCS Final Design for First Plasma, to be completed in 2020. We discuss the objectives of PCSSP, its overall structure, selected features, application to existing devices, and expected evolution over the lifetime of the ITER PCS. We describe an archiving solution for simulation results, methods for incorporating physics models of the plasma and physical plant (tokamak, actuator, and diagnostic systems) into PCSSP, and defining characteristics of models suitable for a plasma control development environment such as PCSSP. Applications of PCSSP simulation models including resistive plasma equilibrium evolution are demonstrated. PCSSP development supported by ITER Organization under ITER/CTS/6000000037. Resistive evolution code developed under General Atomics' Internal funding. The views and opinions expressed herein do not necessarily reflect those of the ITER Organization.

Experiments on Learning by Back Propagation.

ERIC Educational Resources Information Center

Plaut, David C.; And Others

This paper describes further research on a learning procedure for layered networks of deterministic, neuron-like units, described by Rumelhart et al. The units, the way they are connected, the learning procedure, and the extension to iterative networks are presented. In one experiment, a network learns a set of filters, enabling it to discriminate…

Data-Driven Based Asynchronous Motor Control for Printing Servo Systems

NASA Astrophysics Data System (ADS)

Bian, Min; Guo, Qingyun

Modern digital printing equipment aims to the environmental-friendly industry with high dynamic performances and control precision and low vibration and abrasion. High performance motion control system of printing servo systems was required. Control system of asynchronous motor based on data acquisition was proposed. Iterative learning control (ILC) algorithm was studied. PID control was widely used in the motion control. However, it was sensitive to the disturbances and model parameters variation. The ILC applied the history error data and present control signals to approximate the control signal directly in order to fully track the expect trajectory without the system models and structures. The motor control algorithm based on the ILC and PID was constructed and simulation results were given. The results show that data-driven control method is effective dealing with bounded disturbances for the motion control of printing servo systems.

Self-Learning Variable Structure Control for a Class of Sensor-Actuator Systems

PubMed Central

Chen, Sanfeng; Li, Shuai; Liu, Bo; Lou, Yuesheng; Liang, Yongsheng

2012-01-01

Variable structure strategy is widely used for the control of sensor-actuator systems modeled by Euler-Lagrange equations. However, accurate knowledge on the model structure and model parameters are often required for the control design. In this paper, we consider model-free variable structure control of a class of sensor-actuator systems, where only the online input and output of the system are available while the mathematic model of the system is unknown. The problem is formulated from an optimal control perspective and the implicit form of the control law are analytically obtained by using the principle of optimality. The control law and the optimal cost function are explicitly solved iteratively. Simulations demonstrate the effectiveness and the efficiency of the proposed method. PMID:22778633

Active Learning with a Human in The Loop

DTIC Science & Technology

2012-11-01

handwrit - ten digits (LeCun et al. [1998]). In the red curve the model is built iteratively: at each iteration the twenty examples with the lowest...continuum. The most we can say about MUC annotation is that it’s simple enough that other tasks are likely to impose a heavier load on the user for

Learning to Teach Elementary Science through Iterative Cycles of Enactment in Culturally and Linguistically Diverse Contexts

ERIC Educational Resources Information Center

Bottoms, SueAnn I.; Ciechanowski, Kathryn M.; Hartman, Brian

2015-01-01

Iterative cycles of enactment embedded in culturally and linguistically diverse contexts provide rich opportunities for preservice teachers (PSTs) to enact core practices of science. This study is situated in the larger Families Involved in Sociocultural Teaching and Science, Technology, Engineering and Mathematics (FIESTAS) project, which weaves…

Slaying the Great Green Dragon: Learning and Modelling Iterable Ordered Optional Adjuncts

ERIC Educational Resources Information Center

Fowlie, Meaghan

2017-01-01

Adjuncts and arguments exhibit different syntactic behaviours, but modelling this difference in minimalist syntax is challenging: on the one hand, adjuncts differ from arguments in that they are optional, transparent, and iterable, but on the other hand they are often strictly ordered, reflecting the kind of strict selection seen in argument…

Towards a Better Distributed Framework for Learning Big Data

DTIC Science & Technology

2017-06-14

UNLIMITED: PB Public Release 13. SUPPLEMENTARY NOTES 14. ABSTRACT This work aimed at solving issues in distributed machine learning. The PI’s team proposed...communication load. Finally, the team proposed the parallel least-squares policy iteration (parallel LSPI) to parallelize a reinforcement policy learning. 15

Optimizing Chemical Reactions with Deep Reinforcement Learning.

PubMed

Zhou, Zhenpeng; Li, Xiaocheng; Zare, Richard N

2017-12-27

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability.

Scalable Iterative Classification for Sanitizing Large-Scale Datasets

PubMed Central

Li, Bo; Vorobeychik, Yevgeniy; Li, Muqun; Malin, Bradley

2017-01-01

Cheap ubiquitous computing enables the collection of massive amounts of personal data in a wide variety of domains. Many organizations aim to share such data while obscuring features that could disclose personally identifiable information. Much of this data exhibits weak structure (e.g., text), such that machine learning approaches have been developed to detect and remove identifiers from it. While learning is never perfect, and relying on such approaches to sanitize data can leak sensitive information, a small risk is often acceptable. Our goal is to balance the value of published data and the risk of an adversary discovering leaked identifiers. We model data sanitization as a game between 1) a publisher who chooses a set of classifiers to apply to data and publishes only instances predicted as non-sensitive and 2) an attacker who combines machine learning and manual inspection to uncover leaked identifying information. We introduce a fast iterative greedy algorithm for the publisher that ensures a low utility for a resource-limited adversary. Moreover, using five text data sets we illustrate that our algorithm leaves virtually no automatically identifiable sensitive instances for a state-of-the-art learning algorithm, while sharing over 93% of the original data, and completes after at most 5 iterations. PMID:28943741

How can students contribute? A qualitative study of active student involvement in development of technological learning material for clinical skills training.

PubMed

Haraldseid, Cecilie; Friberg, Febe; Aase, Karina

2016-01-01

Policy initiatives and an increasing amount of the literature within higher education both call for students to become more involved in creating their own learning. However, there is a lack of studies in undergraduate nursing education that actively involve students in developing such learning material with descriptions of the students' roles in these interactive processes. Explorative qualitative study, using data from focus group interviews, field notes and student notes. The data has been subjected to qualitative content analysis. Active student involvement through an iterative process identified five different learning needs that are especially important to the students: clarification of learning expectations, help to recognize the bigger picture, stimulation of interaction, creation of structure, and receiving context- specific content. The iterative process involvement of students during the development of new technological learning material will enhance the identification of important learning needs for students. The use of student and teacher knowledge through an adapted co-design process is the most optimal level of that involvement.

Generating Knowledge in a Learning Study--From the Perspective of a Teacher Researcher

ERIC Educational Resources Information Center

Thorsten, Anja

2017-01-01

The purpose of this article is to discuss and describe how a clinical research method can be used to generate knowledge about teaching and learning. This will be addressed from a teacher researcher's perspective, taking a conducted Learning Study as the departure. Learning Study is an interventionist, iterative and collaborative research approach,…

Online Learner Self-Regulation: Learning Presence Viewed through Quantitative Content- and Social Network Analysis

ERIC Educational Resources Information Center

Shea, Peter; Hayes, Suzanne; Smith, Sedef Uzuner; Vickers, Jason; Bidjerano, Temi; Gozza-Cohen, Mary; Jian, Shou-Bang; Pickett, Alexandra M.; Wilde, Jane; Tseng, Chi-Hua

2013-01-01

This paper presents an extension of an ongoing study of online learning framed within the community of inquiry (CoI) model (Garrison, Anderson, & Archer, 2001) in which we further examine a new construct labeled as "learning presence." We use learning presence to refer to the iterative processes of forethought and planning,…

Using Design-Based Research in Informal Environments

ERIC Educational Resources Information Center

Reisman, Molly

2008-01-01

Design-Based Research (DBR) has been a tool of the learning sciences since the early 1990s, used as a way to improve and study learning environments. Using an iterative process of design with the goal of reining theories of learning, researchers and educators now use DBR seek to identify "how" to make a learning environment work. They then draw…

Extortion can outperform generosity in the iterated prisoner's dilemma

PubMed Central

Wang, Zhijian; Zhou, Yanran; Lien, Jaimie W.; Zheng, Jie; Xu, Bin

2016-01-01

Zero-determinant (ZD) strategies, as discovered by Press and Dyson, can enforce a linear relationship between a pair of players' scores in the iterated prisoner's dilemma. Particularly, the extortionate ZD strategies can enforce and exploit cooperation, providing a player with a score advantage, and consequently higher scores than those from either mutual cooperation or generous ZD strategies. In laboratory experiments in which human subjects were paired with computer co-players, we demonstrate that both the generous and the extortionate ZD strategies indeed enforce a unilateral control of the reward. When the experimental setting is sufficiently long and the computerized nature of the opponent is known to human subjects, the extortionate strategy outperforms the generous strategy. Human subjects' cooperation rates when playing against extortionate and generous ZD strategies are similar after learning has occurred. More than half of extortionate strategists finally obtain an average score higher than that from mutual cooperation. PMID:27067513

Norm-Optimal ILC Applied to a High-Speed Rack Feeder

NASA Astrophysics Data System (ADS)

Schindele, Dominik; Aschemann, Harald; Ritzke, Jöran

2010-09-01

Rack feeders as automated conveying systems for high bay rackings are of high practical importance. To shorten the transport times by using trajectories with increased kinematic values accompanying control measures for a reduction of the excited structural vibrations are necessary. In this contribution, the model-based design of a norm-optimal iterative learning control structure is presented. The rack feeder is modelled as an elastic multibody system. For the mathematical description of the bending deflections a Ritz ansatz is introduced. The tracking control design is performed separately for both axes using decentralised state space representations. Both the achievable performance and the resulting tracking accuracy of the proposed control concept are shown by measurement results from the experimental set-up.

Virtual reality cataract surgery training: learning curves and concurrent validity.

PubMed

Selvander, Madeleine; Åsman, Peter

2012-08-01

To investigate initial learning curves on a virtual reality (VR) eye surgery simulator and whether achieved skills are transferable between tasks. Thirty-five medical students were randomized to complete ten iterations on either the VR Caspulorhexis module (group A) or the Cataract navigation training module (group B) and then two iterations on the other module. Learning curves were compared between groups. The second Capsulorhexis video was saved and evaluated with the performance rating tool Objective Structured Assessment of Cataract Surgical Skill (OSACSS). The students' stereoacuity was examined. Both groups demonstrated significant improvements in performance over the 10 iterations: group A for all parameters analysed including score (p < 0.0001), time (p < 0.0001) and corneal damage (p = 0.0003), group B for time (p < 0.0001), corneal damage (p < 0.0001) but not for score (p = 0.752). Training on one module did not improve performance on the other. Capsulorhexis score correlated significantly with evaluation of the videos using the OSACSS performance rating tool. For stereoacuity < and ≥120 seconds of arc, sum of both modules' second iteration score was 73.5 and 41.0, respectively (p = 0.062). An initial rapid improvement in performance on a simulator with repeated practice was shown. For capsulorhexis, 10 iterations with only simulator feedback are not enough to reach a plateau for overall score. Skills transfer between modules was not found suggesting benefits from training on both modules. Stereoacuity may be of importance in the recruitment and training of new cataract surgeons. Additional studies are needed to investigate this further. Concurrent validity was found for Capsulorhexis module. © 2010 The Authors. Acta Ophthalmologica © 2010 Acta Ophthalmologica Scandinavica Foundation.

How children perceive fractals: Hierarchical self-similarity and cognitive development

PubMed Central

Martins, Maurício Dias; Laaha, Sabine; Freiberger, Eva Maria; Choi, Soonja; Fitch, W. Tecumseh

2014-01-01

The ability to understand and generate hierarchical structures is a crucial component of human cognition, available in language, music, mathematics and problem solving. Recursion is a particularly useful mechanism for generating complex hierarchies by means of self-embedding rules. In the visual domain, fractals are recursive structures in which simple transformation rules generate hierarchies of infinite depth. Research on how children acquire these rules can provide valuable insight into the cognitive requirements and learning constraints of recursion. Here, we used fractals to investigate the acquisition of recursion in the visual domain, and probed for correlations with grammar comprehension and general intelligence. We compared second (n = 26) and fourth graders (n = 26) in their ability to represent two types of rules for generating hierarchical structures: Recursive rules, on the one hand, which generate new hierarchical levels; and iterative rules, on the other hand, which merely insert items within hierarchies without generating new levels. We found that the majority of fourth graders, but not second graders, were able to represent both recursive and iterative rules. This difference was partially accounted by second graders’ impairment in detecting hierarchical mistakes, and correlated with between-grade differences in grammar comprehension tasks. Empirically, recursion and iteration also differed in at least one crucial aspect: While the ability to learn recursive rules seemed to depend on the previous acquisition of simple iterative representations, the opposite was not true, i.e., children were able to acquire iterative rules before they acquired recursive representations. These results suggest that the acquisition of recursion in vision follows learning constraints similar to the acquisition of recursion in language, and that both domains share cognitive resources involved in hierarchical processing. PMID:24955884

E-Learning Quality Assurance: A Process-Oriented Lifecycle Model

ERIC Educational Resources Information Center

Abdous, M'hammed

2009-01-01

Purpose: The purpose of this paper is to propose a process-oriented lifecycle model for ensuring quality in e-learning development and delivery. As a dynamic and iterative process, quality assurance (QA) is intertwined with the e-learning development process. Design/methodology/approach: After reviewing the existing literature, particularly…

Infant/Toddler Environment Rating Scale (ITERS-3). Third Edition

ERIC Educational Resources Information Center

Harms, Thelma; Cryer, Debby; Clifford, Richard M.; Yazejian, Noreen

2017-01-01

Building on extensive feedback from the field as well as vigorous new research on how best to support infant and toddler development and learning, the authors have revised and updated the widely used "Infant/Toddler Environment Rating Scale." ITERS-3 is the next-generation assessment tool for use in center-based child care programs for…

Prospects for Advanced Tokamak Operation of ITER

NASA Astrophysics Data System (ADS)

Neilson, George H.

1996-11-01

Previous studies have identified steady-state (or "advanced") modes for ITER, based on reverse-shear profiles and significant bootstrap current. A typical example has 12 MA of plasma current, 1,500 MW of fusion power, and 100 MW of heating and current-drive power. The implementation of these and other steady-state operating scenarios in the ITER device is examined in order to identify key design modifications that can enhance the prospects for successfully achieving advanced tokamak operating modes in ITER compatible with a single null divertor design. In particular, we examine plasma configurations that can be achieved by the ITER poloidal field system with either a monolithic central solenoid (as in the ITER Interim Design), or an alternate "hybrid" central solenoid design which provides for greater flexibility in the plasma shape. The increased control capability and expanded operating space provided by the hybrid central solenoid allows operation at high triangularity (beneficial for improving divertor performance through control of edge-localized modes and for increasing beta limits), and will make it much easier for ITER operators to establish an optimum startup trajectory leading to a high-performance, steady-state scenario. Vertical position control is examined because plasmas made accessible by the hybrid central solenoid can be more elongated and/or less well coupled to the conducting structure. Control of vertical-displacements using the external PF coils remains feasible over much of the expanded operating space. Further work is required to define the full spectrum of axisymmetric plasma disturbances requiring active control In addition to active axisymmetric control, advanced tokamak modes in ITER may require active control of kink modes on the resistive time scale of the conducting structure. This might be accomplished in ITER through the use of active control coils external to the vacuum vessel which are actuated by magnetic sensors near the first wall. The enhanced shaping and positioning flexibility provides a range of options for reducing the ripple-induced losses of fast alpha particles--a major limitation on ITER steady-state modes. An alternate approach that we are pursuing in parallel is the inclusion of ferromagnetic inserts to reduce the toroidal field ripple within the plasma chamber. The inclusion of modest design changes such as the hybrid central solenoid, active control coils for kink modes, and ferromagnetic inserts for TF ripple reduction show can greatly increase the flexibility to accommodate advance tokamak operation in ITER. Increased flexibility is important because the optimum operating scenario for ITER cannot be predicted with certainty. While low-inductance, reverse shear modes appear attractive for steady-state operation, high-inductance, high-beta modes are also viable candidates, and it is important that ITER have the flexibility to explore both these, and other, operating regimes.

Adaptive management for improving species conservation across the captive-wild spectrum

USGS Publications Warehouse

Canessa, Stefano; Guillera-Arroita, Gurutzeta; Lahoz-Monfort, José J.; Southwell, Darren M; Armstrong, Doug P.; Chadès, Iadine; Lacy, Robert C; Converse, Sarah J.

2016-01-01

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah (Acinonyx jubatus) and whooping cranes (Grus americana), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi (Notiomystis cincta) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

Advancing Personalized Learning through the Iterative Application of Innovation Science

ERIC Educational Resources Information Center

Redding, Sam; Twyman, Janet; Murphy, Marilyn

2016-01-01

The promise of personalized learning excites many educators, and schools are wondering how best to introduce it and how they know when they have achieved it. Rather than thinking of personalized learning as an "it" (i.e., a program that is either present or not), we might think of it as an approach to teaching and learning that has many…

We Are the Game Changers: An Open Gaming Literacy Programme

ERIC Educational Resources Information Center

Arnab, Sylvester; Morini, Luca; Green, Kate; Masters, Alex; Bellamy-Woods, Tyrone

2017-01-01

This paper discusses the first iteration of Game Changers Programme hosted by Coventry University's Disruptive Media Learning Lab (DMLL), an open game design initiative. The Programme had the goal of facilitating new models of teaching and learning, new practices in cross-faculty learning/ collaboration to make game design and development more…

Design and Implementation of a Learning Analytics Toolkit for Teachers

ERIC Educational Resources Information Center

Dyckhoff, Anna Lea; Zielke, Dennis; Bultmann, Mareike; Chatti, Mohamed Amine; Schroeder, Ulrik

2012-01-01

Learning Analytics can provide powerful tools for teachers in order to support them in the iterative process of improving the effectiveness of their courses and to collaterally enhance their students' performance. In this paper, we present the theoretical background, design, implementation, and evaluation details of eLAT, a Learning Analytics…

Complexity-Based Learning and Teaching: A Case Study in Higher Education

ERIC Educational Resources Information Center

Fabricatore, Carlo; López, María Ximena

2014-01-01

This paper presents a learning and teaching strategy based on complexity science and explores its impacts on a higher education game design course. The strategy aimed at generating conditions fostering individual and collective learning in educational complex adaptive systems, and led the design of the course through an iterative and adaptive…

Rapid Prototyping of Mobile Learning Games

ERIC Educational Resources Information Center

Federley, Maija; Sorsa, Timo; Paavilainen, Janne; Boissonnier, Kimo; Seisto, Anu

2014-01-01

This position paper presents the first results of an on-going project, in which we explore rapid prototyping method to efficiently produce digital learning solutions that are commercially viable. In this first phase, rapid game prototyping and an iterative approach was tested as a quick and efficient way to create learning games and to evaluate…

Online Learning Tools for Middle School Science: Lessons Learned from a Design-Based Research Project

ERIC Educational Resources Information Center

Terrazas-Arellanes, Fatima E.; Knox, Carolyn; Strycker, Lisa A.; Walden, Emily D.

2017-01-01

This article reports on how design-based research methodology was used to guide a line of intervention research that developed, implemented, revised, and evaluated online learning science curricula for middle school students, including general education students and English language learners (primarily of Hispanic origin). The iterative,…

Ask-the-expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika; Avrekh, Ilya; Matthews, Bryan; Sharma, Manali; Oza, Nikunj

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the backend. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Ask-the-Expert: Active Learning Based Knowledge Discovery Using the Expert

NASA Technical Reports Server (NTRS)

Das, Kamalika

2017-01-01

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is an iterative process of learning a classifier based on a small number of SME-provided labels at each iteration, the lack of an enabling tool can hinder the process of adoption of these technologies in real-life, in spite of their labor-saving potential. In this demo we present ASK-the-Expert, an interactive tool that allows SMEs to review instances from a data set and provide labels within a single framework. ASK-the-Expert is powered by an active learning algorithm for training a classifier in the back end. We demonstrate this system in the context of an aviation safety application, but the tool can be adopted to work as a simple review and labeling tool as well, without the use of active learning.

Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning

2018-06-01

Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.

From Intent to Action: An Iterative Engineering Process

ERIC Educational Resources Information Center

Mouton, Patrice; Rodet, Jacques; Vacaresse, Sylvain

2015-01-01

Quite by chance, and over the course of a few haphazard meetings, a Master's degree in "E-learning Design" gradually developed in a Faculty of Economics. Its original and evolving design was the result of an iterative process carried out, not by a single Instructional Designer (ID), but by a full ID team. Over the last 10 years it has…

A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization Problems.

PubMed

Gong, Pinghua; Zhang, Changshui; Lu, Zhaosong; Huang, Jianhua Z; Ye, Jieping

2013-01-01

Non-convex sparsity-inducing penalties have recently received considerable attentions in sparse learning. Recent theoretical investigations have demonstrated their superiority over the convex counterparts in several sparse learning settings. However, solving the non-convex optimization problems associated with non-convex penalties remains a big challenge. A commonly used approach is the Multi-Stage (MS) convex relaxation (or DC programming), which relaxes the original non-convex problem to a sequence of convex problems. This approach is usually not very practical for large-scale problems because its computational cost is a multiple of solving a single convex problem. In this paper, we propose a General Iterative Shrinkage and Thresholding (GIST) algorithm to solve the nonconvex optimization problem for a large class of non-convex penalties. The GIST algorithm iteratively solves a proximal operator problem, which in turn has a closed-form solution for many commonly used penalties. At each outer iteration of the algorithm, we use a line search initialized by the Barzilai-Borwein (BB) rule that allows finding an appropriate step size quickly. The paper also presents a detailed convergence analysis of the GIST algorithm. The efficiency of the proposed algorithm is demonstrated by extensive experiments on large-scale data sets.

Manager Perspectives on Communication and Public ...

EPA Pesticide Factsheets

We argue that public engagement is crucial to achieving lasting ecological success in aquatic restoration efforts, and that the most effective public engagement mechanisms are what we term iterative mechanisms. Here we look to a particular social-ecological system – the restoration community in Rhode Island, U.S.A. and the rivers, wetlands, marshes, and estuaries, and their related species, that they work to protect – to better understand land managers’ perspectives on public engagement in restoration processes. Adopting an inductive approach to critical discourse analysis of interviews with 27 local, state, and federal restoration managers and the forms of public interaction they described, we identify three distinct models of public engagement in natural resources management employed by managers: unidirectional; bidirectional; and iterative. While unidirectional and bidirectional mechanisms can help managers achieve short-term ecological successes, we suggest that adopting an iterative approach can improve the quality of stakeholder and learning interactions and, subsequently, foster lasting ecological successes. We argue that managers can design deliberately for public engagement mechanisms that are best suited to projects in particular social-ecological systems in order to create restoration projects that achieve ecological, learning, and stakeholder successes. We attempt to synthesize the lessons learned from efforts at public engagement in restoratio

Sequence-Controlled Polymerization on Facially Amphiphilic Templates at Interfaces

DTIC Science & Technology

2016-06-14

controlled chain growth polymerization. We will synthesize a ?- conjugated “parent” polymer by iterative exponential growth (IEG), attach cyclic olefin...template that is programmed to direct sequence- controlled chain growth polymerization. We will synthesize a ?- conjugated “parent” polymer by iterative...polymerization. We will synthesize a π- conjugated “parent” polymer by organometallic iterative exponential growth (IEG),2 attach cyclic olefin “daughter

Optimizing Chemical Reactions with Deep Reinforcement Learning

PubMed Central

2017-01-01

Deep reinforcement learning was employed to optimize chemical reactions. Our model iteratively records the results of a chemical reaction and chooses new experimental conditions to improve the reaction outcome. This model outperformed a state-of-the-art blackbox optimization algorithm by using 71% fewer steps on both simulations and real reactions. Furthermore, we introduced an efficient exploration strategy by drawing the reaction conditions from certain probability distributions, which resulted in an improvement on regret from 0.062 to 0.039 compared with a deterministic policy. Combining the efficient exploration policy with accelerated microdroplet reactions, optimal reaction conditions were determined in 30 min for the four reactions considered, and a better understanding of the factors that control microdroplet reactions was reached. Moreover, our model showed a better performance after training on reactions with similar or even dissimilar underlying mechanisms, which demonstrates its learning ability. PMID:29296675

Modelling of edge localised modes and edge localised mode control [Modelling of ELMs and ELM control

DOE PAGES

Huijsmans, G. T. A.; Chang, C. S.; Ferraro, N.; ...

2015-02-07

Edge Localised Modes (ELMs) in ITER Q = 10 H-mode plasmas are likely to lead to large transient heat loads to the divertor. In order to avoid an ELM induced reduction of the divertor lifetime, the large ELM energy losses need to be controlled. In ITER, ELM control is foreseen using magnetic field perturbations created by in-vessel coils and the injection of small D2 pellets. ITER plasmas are characterised by low collisionality at a high density (high fraction of the Greenwald density limit). These parameters cannot simultaneously be achieved in current experiments. Thus, the extrapolation of the ELM properties andmore » the requirements for ELM control in ITER relies on the development of validated physics models and numerical simulations. Here, we describe the modelling of ELMs and ELM control methods in ITER. The aim of this paper is not a complete review on the subject of ELM and ELM control modelling but rather to describe the current status and discuss open issues.« less

Controlling a multi-degree of freedom upper limb prosthesis using foot controls: user experience.

PubMed

Resnik, Linda; Klinger, Shana Lieberman; Etter, Katherine; Fantini, Christopher

2014-07-01

The DEKA Arm, a pre-commercial upper limb prosthesis, funded by the DARPA Revolutionizing Prosthetics Program, offers increased degrees of freedom while requiring a large number of user control inputs to operate. To address this challenge, DEKA developed prototype foot controls. Although the concept of utilizing foot controls to operate an upper limb prosthesis has been discussed for decades, only small-sized studies have been performed and no commercial product exists. The purpose of this paper is to report amputee user perspectives on using three different iterations of foot controls to operate the DEKA Arm. Qualitative data was collected from 36 subjects as part of the Department of Veterans Affairs (VA) Study to Optimize the DEKA Arm through surveys, interviews, audio memos, and videotaped sessions. Three major, interrelated themes were identified using the constant comparative method: attitudes towards foot controls, psychomotor learning and physical experience of using foot controls. Feedback about foot controls was generally positive for all iterations. The final version of foot controls was viewed most favorably. Our findings indicate that foot controls are a viable control option that can enable control of a multifunction upper limb prosthesis (the DEKA Arm). Multifunction upper limb prostheses require many user control inputs to operate. Foot controls offer additional control input options for such advanced devices, yet have had minimal study. This study found that foot controls were a viable option for controlling multifunction upper limb prostheses. Most of the 36 subjects in this study were willing to adopt foot controls to control the multiple degrees of freedom of the DEKA Arm. With training and practice, all users were able to develop the psychomotor skills needed to successfully operate food controls. Some had initial difficulty, but acclimated over time.

Neural network-based optimal adaptive output feedback control of a helicopter UAV.

PubMed

Nodland, David; Zargarzadeh, Hassan; Jagannathan, Sarangapani

2013-07-01

Helicopter unmanned aerial vehicles (UAVs) are widely used for both military and civilian operations. Because the helicopter UAVs are underactuated nonlinear mechanical systems, high-performance controller design for them presents a challenge. This paper introduces an optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN). The output-feedback control system utilizes the backstepping methodology, employing kinematic and dynamic controllers and an NN observer. The online approximator-based dynamic controller learns the infinite-horizon Hamilton-Jacobi-Bellman equation in continuous time and calculates the corresponding optimal control input by minimizing a cost function, forward-in-time, without using the value and policy iterations. Optimal tracking is accomplished by using a single NN utilized for the cost function approximation. The overall closed-loop system stability is demonstrated using Lyapunov analysis. Finally, simulation results are provided to demonstrate the effectiveness of the proposed control design for trajectory tracking.

Drawing Analogies to Deepen Learning

ERIC Educational Resources Information Center

Fava, Michelle

2017-01-01

This article offers examples of how drawing can facilitate thinking skills that promote analogical reasoning to enable deeper learning. The instructional design applies cognitive principles, briefly described here. The workshops were developed iteratively, through feedback from student and teacher participants. Elements of the UK National…

Socio-Technical Dimensions of an Outdoor Mobile Learning Environment: A Three-Phase Design-Based Research Investigation

ERIC Educational Resources Information Center

Land, Susan M.; Zimmerman, Heather Toomey

2015-01-01

This design-based research project examines three iterations of Tree Investigators, a learning environment designed to support science learning outdoors at an arboretum and nature center using mobile devices (iPads). Researchers coded videorecords and artifacts created by children and parents (n = 53) to understand how both social and…

Contextual EFL Learning in a 3D Virtual Environment

ERIC Educational Resources Information Center

Lan, Yu-Ju

2015-01-01

The purposes of the current study are to develop virtually immersive EFL learning contexts for EFL learners in Taiwan to pre- and review English materials beyond the regular English class schedule. A 2-iteration action research lasting for one semester was conducted to evaluate the effects of virtual contexts on learners' EFL learning. 132…

Towards Collaboration as Learning: Evaluation of an Open CPD Opportunity for HE Teachers

ERIC Educational Resources Information Center

Nerantzi, Chrissi; Gossman, Peter

2015-01-01

Flexible, Distance and Online Learning (FDOL) is an open online course offered as an informal cross-institutional collaboration based on a postgraduate module in the context of teacher education in higher education. The second iteration, FDOL132, was offered in 2013 using a problem-based learning (PBL) design (FISh) to foster collaborative…

Reflections on the Use of Iterative, Agile and Collaborative Approaches for Blended Flipped Learning Development

ERIC Educational Resources Information Center

Owen, Hazel; Dunham, Nicola

2015-01-01

E-learning experiences are widely becoming common practice in many schools, tertiary institutions and other organisations. However despite this increased use of technology to enhance learning and the associated investment involved the result does not always equate to more engaged, knowledgeable and skilled learners. We have observed two key…

Factors Contributing to Cognitive Absorption and Grounded Learning Effectiveness in a Competitive Business Marketing Simulation

ERIC Educational Resources Information Center

Baker, David Scott; Underwood, James, III; Thakur, Ramendra

2017-01-01

This study aimed to establish a pedagogical positioning of a business marketing simulation as a grounded learning teaching tool and empirically assess the dimensions of cognitive absorption related to grounded learning effectiveness in an iterative business simulation environment. The method/design and sample consisted of a field study survey…

The Iterative Development and Use of an Online Problem-Based Learning Module for Preservice and Inservice Teachers

ERIC Educational Resources Information Center

Rillero, Peter; Camposeco, Laurie

2018-01-01

Teachers' problem-based learning knowledge, abilities, and attitudes are important factors in successful K--12 PBL implementations. This article describes the development and use of a free, online module entitled "Design a Problem-Based Learning Experience." The module production, aligned with theories of andragogy, was a partnership…

Exploiting Multi-Step Sample Trajectories for Approximate Value Iteration

DTIC Science & Technology

2013-09-01

WORK UNIT NUMBER IH 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) AFRL/ RISC 525 Brooks Road, Rome NY 13441-4505 Binghamton University...S) AND ADDRESS(ES) Air Force Research Laboratory/Information Directorate Rome Research Site/ RISC 525 Brooks Road Rome NY 13441-4505 10. SPONSOR...iteration methods for reinforcement learning (RL) generalize experience from limited samples across large state-action spaces. The function approximators

Imaging complex objects using learning tomography

NASA Astrophysics Data System (ADS)

Lim, JooWon; Goy, Alexandre; Shoreh, Morteza Hasani; Unser, Michael; Psaltis, Demetri

2018-02-01

Optical diffraction tomography (ODT) can be described using the scattering process through an inhomogeneous media. An inherent nonlinearity exists relating the scattering medium and the scattered field due to multiple scattering. Multiple scattering is often assumed to be negligible in weakly scattering media. This assumption becomes invalid as the sample gets more complex resulting in distorted image reconstructions. This issue becomes very critical when we image a complex sample. Multiple scattering can be simulated using the beam propagation method (BPM) as the forward model of ODT combined with an iterative reconstruction scheme. The iterative error reduction scheme and the multi-layer structure of BPM are similar to neural networks. Therefore we refer to our imaging method as learning tomography (LT). To fairly assess the performance of LT in imaging complex samples, we compared LT with the conventional iterative linear scheme using Mie theory which provides the ground truth. We also demonstrate the capacity of LT to image complex samples using experimental data of a biological cell.

High-bandwidth and flexible tracking control for precision motion with application to a piezo nanopositioner.

PubMed

Feng, Zhao; Ling, Jie; Ming, Min; Xiao, Xiao-Hui

2017-08-01

For precision motion, high-bandwidth and flexible tracking are the two important issues for significant performance improvement. Iterative learning control (ILC) is an effective feedforward control method only for systems that operate strictly repetitively. Although projection ILC can track varying references, the performance is still limited by the fixed-bandwidth Q-filter, especially for triangular waves tracking commonly used in a piezo nanopositioner. In this paper, a wavelet transform-based linear time-varying (LTV) Q-filter design for projection ILC is proposed to compensate high-frequency errors and improve the ability to tracking varying references simultaneously. The LVT Q-filter is designed based on the modulus maximum of wavelet detail coefficients calculated by wavelet transform to determine the high-frequency locations of each iteration with the advantages of avoiding cross-terms and segmenting manually. The proposed approach was verified on a piezo nanopositioner. Experimental results indicate that the proposed approach can locate the high-frequency regions accurately and achieve the best performance under varying references compared with traditional frequency-domain and projection ILC with a fixed-bandwidth Q-filter, which validates that through implementing the LTV filter on projection ILC, high-bandwidth and flexible tracking can be achieved simultaneously by the proposed approach.

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor)

1990-01-01

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Keeping the Bootcamp Fun Alive!

EPA Science Inventory

This product is a blog post that outlines a course conducted to build on skills learned in a Software Carpentry Bootcamp co-hosted by AED researcher, Jeff Hollister. The post provides details on the course and some lessons learned that will be implemented in future iterations of...

Defining and Building an Enriched Learning and Information Environment.

ERIC Educational Resources Information Center

Goodrum, David A.; And Others

1993-01-01

Discusses the development of an Enriched Learning and Information Environment (ELIE). Highlights include technology-based and theory-based frameworks for defining ELIEs; a socio-technical definition; a conceptual prototype; a participatory design process, including iterative design through rapid prototyping; and design issues for technology…

"There Was a Lot of Learning Going on" Using a Digital Medium to Support Learning in a Professional Course for New HE Lecturers

ERIC Educational Resources Information Center

Chesney, Sarah; Marcangelo, Caroline

2010-01-01

This small scale action research study investigated the experiences of learners over two iterations as they completed a patchwork text assignment within the digital medium of a personal learning system (PLS). The aim was to investigate the extent to which using a PLS can facilitate formative and collaborative feedback to assist student learning. A…

A Cost-Effectiveness Analysis of Blended Versus Face-to-Face Delivery of Evidence-Based Medicine to Medical Students.

PubMed

Maloney, Stephen; Nicklen, Peter; Rivers, George; Foo, Jonathan; Ooi, Ying Ying; Reeves, Scott; Walsh, Kieran; Ilic, Dragan

2015-07-21

Blended learning describes a combination of teaching methods, often utilizing digital technologies. Research suggests that learner outcomes can be improved through some blended learning formats. However, the cost-effectiveness of delivering blended learning is unclear. This study aimed to determine the cost-effectiveness of a face-to-face learning and blended learning approach for evidence-based medicine training within a medical program. The economic evaluation was conducted as part of a randomized controlled trial (RCT) comparing the evidence-based medicine (EBM) competency of medical students who participated in two different modes of education delivery. In the traditional face-to-face method, students received ten 2-hour classes. In the blended learning approach, students received the same total face-to-face hours but with different activities and additional online and mobile learning. Online activities utilized YouTube and a library guide indexing electronic databases, guides, and books. Mobile learning involved self-directed interactions with patients in their regular clinical placements. The attribution and differentiation of costs between the interventions within the RCT was measured in conjunction with measured outcomes of effectiveness. An incremental cost-effectiveness ratio was calculated comparing the ongoing operation costs of each method with the level of EBM proficiency achieved. Present value analysis was used to calculate the break-even point considering the transition cost and the difference in ongoing operation cost. The incremental cost-effectiveness ratio indicated that it costs 24% less to educate a student to the same level of EBM competency via the blended learning approach used in the study, when excluding transition costs. The sunk cost of approximately AUD $40,000 to transition to the blended model exceeds any savings from using the approach within the first year of its implementation; however, a break-even point is achieved within its third iteration and relative savings in the subsequent years. The sensitivity analysis indicates that approaches with higher transition costs, or staffing requirements over that of a traditional method, are likely to result in negative value propositions. Under the study conditions, a blended learning approach was more cost-effective to operate and resulted in improved value for the institution after the third year iteration, when compared to the traditional face-to-face model. The wider applicability of the findings are dependent on the type of blended learning utilized, staffing expertise, and educational context.

A Faculty Professional Development Model That Improves Student Learning, Encourages Active-Learning Instructional Practices, and Works for Faculty at Multiple Institutions.

PubMed

Pelletreau, Karen N; Knight, Jennifer K; Lemons, Paula P; McCourt, Jill S; Merrill, John E; Nehm, Ross H; Prevost, Luanna B; Urban-Lurain, Mark; Smith, Michelle K

2018-06-01

Helping faculty develop high-quality instruction that positively affects student learning can be complicated by time limitations, a lack of resources, and inexperience using student data to make iterative improvements. We describe a community of 16 faculty from five institutions who overcame these challenges and collaboratively designed, taught, iteratively revised, and published an instructional unit about the potential effect of mutations on DNA replication, transcription, and translation. The unit was taught to more than 2000 students in 18 courses, and student performance improved from preassessment to postassessment in every classroom. This increase occurred even though faculty varied in their instructional practices when they were teaching identical materials. We present information on how this faculty group was organized and facilitated, how members used student data to positively affect learning, and how they increased their use of active-learning instructional practices in the classroom as a result of participation. We also interviewed faculty to learn more about the most useful components of the process. We suggest that this professional development model can be used for geographically separated faculty who are interested in working together on a known conceptual difficulty to improve student learning and explore active-learning instructional practices.

Online Pairwise Learning Algorithms.

PubMed

Ying, Yiming; Zhou, Ding-Xuan

2016-04-01

Pairwise learning usually refers to a learning task that involves a loss function depending on pairs of examples, among which the most notable ones are bipartite ranking, metric learning, and AUC maximization. In this letter we study an online algorithm for pairwise learning with a least-square loss function in an unconstrained setting of a reproducing kernel Hilbert space (RKHS) that we refer to as the Online Pairwise lEaRning Algorithm (OPERA). In contrast to existing works (Kar, Sriperumbudur, Jain, & Karnick, 2013 ; Wang, Khardon, Pechyony, & Jones, 2012 ), which require that the iterates are restricted to a bounded domain or the loss function is strongly convex, OPERA is associated with a non-strongly convex objective function and learns the target function in an unconstrained RKHS. Specifically, we establish a general theorem that guarantees the almost sure convergence for the last iterate of OPERA without any assumptions on the underlying distribution. Explicit convergence rates are derived under the condition of polynomially decaying step sizes. We also establish an interesting property for a family of widely used kernels in the setting of pairwise learning and illustrate the convergence results using such kernels. Our methodology mainly depends on the characterization of RKHSs using its associated integral operators and probability inequalities for random variables with values in a Hilbert space.

An iterated Laplacian based semi-supervised dimensionality reduction for classification of breast cancer on ultrasound images.

PubMed

Liu, Xiao; Shi, Jun; Zhou, Shichong; Lu, Minhua

2014-01-01

The dimensionality reduction is an important step in ultrasound image based computer-aided diagnosis (CAD) for breast cancer. A newly proposed l2,1 regularized correntropy algorithm for robust feature selection (CRFS) has achieved good performance for noise corrupted data. Therefore, it has the potential to reduce the dimensions of ultrasound image features. However, in clinical practice, the collection of labeled instances is usually expensive and time costing, while it is relatively easy to acquire the unlabeled or undetermined instances. Therefore, the semi-supervised learning is very suitable for clinical CAD. The iterated Laplacian regularization (Iter-LR) is a new regularization method, which has been proved to outperform the traditional graph Laplacian regularization in semi-supervised classification and ranking. In this study, to augment the classification accuracy of the breast ultrasound CAD based on texture feature, we propose an Iter-LR-based semi-supervised CRFS (Iter-LR-CRFS) algorithm, and then apply it to reduce the feature dimensions of ultrasound images for breast CAD. We compared the Iter-LR-CRFS with LR-CRFS, original supervised CRFS, and principal component analysis. The experimental results indicate that the proposed Iter-LR-CRFS significantly outperforms all other algorithms.

Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-09-01

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.

Learning to See the Infinite: Measuring Visual Literacy Skills in a 1st-Year Seminar Course

ERIC Educational Resources Information Center

Palmer, Michael S.; Matthews, Tatiana

2015-01-01

Visual literacy was a stated learning objective for the fall 2009 iteration of a first-year seminar course. To help students develop visual literacy skills, they received formal instruction throughout the semester and completed a series of carefully designed learning activities. The effects of these interventions were measured using a one-group…

Integrating a MOOC into the Postgraduate ELT Curriculum: Reflecting on Students' Beliefs with a MOOC Blend

ERIC Educational Resources Information Center

Orsini-Jones, Marina; Gafaro, Barbara Conde; Altamimi, Shooq

2017-01-01

This chapter builds on the outcomes of a blended learning action-research project in its third iteration (academic year 2015-16). The FutureLearn Massive Open Online Course (MOOC) "Understanding Language: Learning and Teaching" was integrated into the curriculum of the Master of Arts (MA) in English Language Teaching (ELT) at Coventry…

Situated Cognition and Learning Environments: Implications for Teachers On- and Offline in the New Digital Media Age

ERIC Educational Resources Information Center

Gomez, Kimberley; Lee, Ung-Sang

2015-01-01

John Seely Brown suggested that learning environments should be spaces in which all work is public, is subject to iterative critique by instructors and peers, and in which social interaction is primary. In such spaces, students and teachers engage in a situated cognition approach to teaching and learning where "cognitive accomplishments rely…

Quantitative Reasoning in Environmental Science: A Learning Progression

ERIC Educational Resources Information Center

Mayes, Robert Lee; Forrester, Jennifer Harris; Christus, Jennifer Schuttlefield; Peterson, Franziska Isabel; Bonilla, Rachel; Yestness, Nissa

2014-01-01

The ability of middle and high school students to reason quantitatively within the context of environmental science was investigated. A quantitative reasoning (QR) learning progression was created with three progress variables: quantification act, quantitative interpretation, and quantitative modeling. An iterative research design was used as it…

Eliminating Unpredictable Variation through Iterated Learning

ERIC Educational Resources Information Center

Smith, Kenny; Wonnacott, Elizabeth

2010-01-01

Human languages may be shaped not only by the (individual psychological) processes of language acquisition, but also by population-level processes arising from repeated language learning and use. One prevalent feature of natural languages is that they avoid unpredictable variation. The current work explores whether linguistic predictability might…

Quadrotor Control in the Presence of Unknown Mass Properties

NASA Astrophysics Data System (ADS)

Duivenvoorden, Rikky Ricardo Petrus Rufino

Quadrotor UAVs are popular due to their mechanical simplicity, as well as their capability to hover and vertically take-off and land. As applications diversify, quadrotors are increasingly required to operate under unknown mass properties, for example as a multirole sensor platform or for package delivery operations. The work presented here consists of the derivation of a generalized quadrotor dynamic model without the typical simplifying assumptions on the first and second moments of mass. The maximum payload capacity of a quadrotor in hover, and the observability of the unknown mass properties are discussed. A brief introduction of L1 adaptive control is provided, and three different L 1 adaptive controllers were designed for the Parrot AR.Drone quadrotor. Their tracking and disturbance rejection performance was compared to the baseline nonlinear controller in experiments. Finally, the results of the combination of L1 adaptive control with iterative learning control are presented, showing high performance trajectory tracking under uncertainty.

Multi-Innovation Gradient Iterative Locally Weighted Learning Identification for A Nonlinear Ship Maneuvering System

NASA Astrophysics Data System (ADS)

Bai, Wei-wei; Ren, Jun-sheng; Li, Tie-shan

2018-06-01

This paper explores a highly accurate identification modeling approach for the ship maneuvering motion with fullscale trial. A multi-innovation gradient iterative (MIGI) approach is proposed to optimize the distance metric of locally weighted learning (LWL), and a novel non-parametric modeling technique is developed for a nonlinear ship maneuvering system. This proposed method's advantages are as follows: first, it can avoid the unmodeled dynamics and multicollinearity inherent to the conventional parametric model; second, it eliminates the over-learning or underlearning and obtains the optimal distance metric; and third, the MIGI is not sensitive to the initial parameter value and requires less time during the training phase. These advantages result in a highly accurate mathematical modeling technique that can be conveniently implemented in applications. To verify the characteristics of this mathematical model, two examples are used as the model platforms to study the ship maneuvering.

Intelligent cooperation: A framework of pedagogic practice in the operating room.

PubMed

Sutkin, Gary; Littleton, Eliza B; Kanter, Steven L

2018-04-01

Surgeons who work with trainees must address their learning needs without compromising patient safety. We used a constructivist grounded theory approach to examine videos of five teaching surgeries. Attending surgeons were interviewed afterward while watching cued videos of their cases. Codes were iteratively refined into major themes, and then constructed into a larger framework. We present a novel framework, Intelligent Cooperation, which accounts for the highly adaptive, iterative features of surgical teaching in the operating room. Specifically, we define Intelligent Cooperation as a sequence of coordinated exchanges between attending and trainee that accomplishes small surgical steps while simultaneously uncovering the trainee's learning needs. Intelligent Cooperation requires the attending to accurately determine learning needs, perform real-time needs assessment, provide critical scaffolding, and work with the learner to accomplish the next step in the surgery. This is achieved through intense, coordinated verbal and physical cooperation. Copyright © 2017 Elsevier Inc. All rights reserved.

Progress on the application of ELM control schemes to ITER scenarios from the non-active phase to DT operation

NASA Astrophysics Data System (ADS)

Loarte, A.; Huijsmans, G.; Futatani, S.; Baylor, L. R.; Evans, T. E.; Orlov, D. M.; Schmitz, O.; Becoulet, M.; Cahyna, P.; Gribov, Y.; Kavin, A.; Sashala Naik, A.; Campbell, D. J.; Casper, T.; Daly, E.; Frerichs, H.; Kischner, A.; Laengner, R.; Lisgo, S.; Pitts, R. A.; Saibene, G.; Wingen, A.

2014-03-01

Progress in the definition of the requirements for edge localized mode (ELM) control and the application of ELM control methods both for high fusion performance DT operation and non-active low-current operation in ITER is described. Evaluation of the power fluxes for low plasma current H-modes in ITER shows that uncontrolled ELMs will not lead to damage to the tungsten (W) divertor target, unlike for high-current H-modes in which divertor damage by uncontrolled ELMs is expected. Despite the lack of divertor damage at lower currents, ELM control is found to be required in ITER under these conditions to prevent an excessive contamination of the plasma by W, which could eventually lead to an increased disruptivity. Modelling with the non-linear MHD code JOREK of the physics processes determining the flow of energy from the confined plasma onto the plasma-facing components during ELMs at the ITER scale shows that the relative contribution of conductive and convective losses is intrinsically linked to the magnitude of the ELM energy loss. Modelling of the triggering of ELMs by pellet injection for DIII-D and ITER has identified the minimum pellet size required to trigger ELMs and, from this, the required fuel throughput for the application of this technique to ITER is evaluated and shown to be compatible with the installed fuelling and tritium re-processing capabilities in ITER. The evaluation of the capabilities of the ELM control coil system in ITER for ELM suppression is carried out (in the vacuum approximation) and found to have a factor of ˜2 margin in terms of coil current to achieve its design criterion, although such a margin could be substantially reduced when plasma shielding effects are taken into account. The consequences for the spatial distribution of the power fluxes at the divertor of ELM control by three-dimensional (3D) fields are evaluated and found to lead to substantial toroidal asymmetries in zones of the divertor target away from the separatrix. Therefore, specifications for the rotation of the 3D perturbation applied for ELM control in order to avoid excessive localized erosion of the ITER divertor target are derived. It is shown that a rotation frequency in excess of 1 Hz for the whole toroidally asymmetric divertor power flux pattern is required (corresponding to n Hz frequency in the variation of currents in the coils, where n is the toroidal symmetry of the perturbation applied) in order to avoid unacceptable thermal cycling of the divertor target for the highest power fluxes and worst toroidal power flux asymmetries expected. The possible use of the in-vessel vertical stability coils for ELM control as a back-up to the main ELM control systems in ITER is described and the feasibility of its application to control ELMs in low plasma current H-modes, foreseen for initial ITER operation, is evaluated and found to be viable for plasma currents up to 5-10 MA depending on modelling assumptions.

Using concept maps and goal-setting to support the development of self-regulated learning in a problem-based learning curriculum.

PubMed

Thomas, Lisa; Bennett, Sue; Lockyer, Lori

2016-09-01

Problem-based learning (PBL) in medical education focuses on preparing independent learners for continuing, self-directed, professional development beyond the classroom. Skills in self-regulated learning (SRL) are important for success in PBL and ongoing professional practice. However, the development of SRL skills is often left to chance. This study presents the investigated outcomes for students when support for the development of SRL was embedded in a PBL medical curriculum. This investigation involved design, delivery and testing of SRL support, embedded into the first phase of a four-year, graduate-entry MBBS degree. The intervention included concept mapping and goal-setting activities through iterative processes of planning, monitoring and reflecting on learning. A mixed-methods approach was used to collect data from seven students to develop case studies of engagement with, and outcomes from, the SRL support. The findings indicate that students who actively engaged with support for SRL demonstrated increases in cognitive and metacognitive functioning. Students also reported a greater sense of confidence in and control over their approaches to learning in PBL. This study advances understanding about how the development of SRL can be integrated into PBL.

Engineering Design Theory: Applying the Success of the Modern World to Campaign Creation

DTIC Science & Technology

2009-05-21

and school of thought) to the simple methods of design.6 This progression is analogous to Peter Senge’s levels of learning disciplines.7 Senge...iterative learning and adaptive action that develops and employs critical and creative thinking , enabling leaders to apply the necessary logic to...overcome mental rigidity and develop group insight, the Army must learn to utilize group learning and thinking , through a fluid and creative open process

Exploring emerging learning needs: a UK-wide consultation on environmental sustainability learning objectives for medical education.

PubMed

Walpole, Sarah C; Mortimer, Frances; Inman, Alice; Braithwaite, Isobel; Thompson, Trevor

2015-12-24

This study aimed to engage wide-ranging stakeholders and develop consensus learning objectives for undergraduate and postgraduate medical education. A UK-wide consultation garnered opinions of healthcare students, healthcare educators and other key stakeholders about environmental sustainability in medical education. The policy Delphi approach informed this study. Draft learning objectives were revised iteratively during three rounds of consultation: online questionnaire or telephone interview, face-to-face seminar and email consultation. Twelve draft learning objectives were developed based on review of relevant literature. In round one, 64 participants' median ratings of the learning objectives were 3.5 for relevance and 3.0 for feasibility on a Likert scale of one to four. Revisions were proposed, e.g. to highlight relevance to public health and professionalism. Thirty three participants attended round two. Conflicting opinions were explored. Added content areas included health benefits of sustainable behaviours. To enhance usability, restructuring provided three overarching learning objectives, each with subsidiary points. All participants from rounds one and two were contacted in round three, and no further edits were required. This is the first attempt to define consensus learning objectives for medical students about environmental sustainability. Allowing a wide range of stakeholders to comment on multiple iterations of the document stimulated their engagement with the issues raised and ownership of the resulting learning objectives.

Cognitive Model of Trust Dynamics Predicts Human Behavior within and between Two Games of Strategic Interaction with Computerized Confederate Agents

PubMed Central

Collins, Michael G.; Juvina, Ion; Gluck, Kevin A.

2016-01-01

When playing games of strategic interaction, such as iterated Prisoner's Dilemma and iterated Chicken Game, people exhibit specific within-game learning (e.g., learning a game's optimal outcome) as well as transfer of learning between games (e.g., a game's optimal outcome occurring at a higher proportion when played after another game). The reciprocal trust players develop during the first game is thought to mediate transfer of learning effects. Recently, a computational cognitive model using a novel trust mechanism has been shown to account for human behavior in both games, including the transfer between games. We present the results of a study in which we evaluate the model's a priori predictions of human learning and transfer in 16 different conditions. The model's predictive validity is compared against five model variants that lacked a trust mechanism. The results suggest that a trust mechanism is necessary to explain human behavior across multiple conditions, even when a human plays against a non-human agent. The addition of a trust mechanism to the other learning mechanisms within the cognitive architecture, such as sequence learning, instance-based learning, and utility learning, leads to better prediction of the empirical data. It is argued that computational cognitive modeling is a useful tool for studying trust development, calibration, and repair. PMID:26903892

Accelerating Learning By Neural Networks

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad; Barhen, Jacob

1992-01-01

Electronic neural networks made to learn faster by use of terminal teacher forcing. Method of supervised learning involves addition of teacher forcing functions to excitations fed as inputs to output neurons. Initially, teacher forcing functions are strong enough to force outputs to desired values; subsequently, these functions decay with time. When learning successfully completed, terminal teacher forcing vanishes, and dynamics or neural network become equivalent to those of conventional neural network. Simulated neural network with terminal teacher forcing learned to produce close approximation of circular trajectory in 400 iterations.

Fast machine-learning online optimization of ultra-cold-atom experiments.

PubMed

Wigley, P B; Everitt, P J; van den Hengel, A; Bastian, J W; Sooriyabandara, M A; McDonald, G D; Hardman, K S; Quinlivan, C D; Manju, P; Kuhn, C C N; Petersen, I R; Luiten, A N; Hope, J J; Robins, N P; Hush, M R

2016-05-16

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our 'learner' discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system.

Fast machine-learning online optimization of ultra-cold-atom experiments

PubMed Central

Wigley, P. B.; Everitt, P. J.; van den Hengel, A.; Bastian, J. W.; Sooriyabandara, M. A.; McDonald, G. D.; Hardman, K. S.; Quinlivan, C. D.; Manju, P.; Kuhn, C. C. N.; Petersen, I. R.; Luiten, A. N.; Hope, J. J.; Robins, N. P.; Hush, M. R.

2016-01-01

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates (BEC). BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ‘learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. PMID:27180805

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

PubMed

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

PubMed

Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2015-05-01

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

A Strategic Planning Process Model for Distance Education

ERIC Educational Resources Information Center

Pisel, Kenneth P.

2008-01-01

As more institutions seek to implement or expand distance learning programs, it becomes critical to integrate distance learning programs into broader strategic visions and plans. Using the informed opinion from a panel of peer-nominated experts via iterative Delphi questionnaires, a 10-phased strategic planning process model for distance education…

Origami: An Active Learning Exercise for Scrum Project Management

ERIC Educational Resources Information Center

Sibona, Christopher; Pourreza, Saba; Hill, Stephen

2018-01-01

Scrum is a popular project management model for iterative delivery of software that subscribes to Agile principles. This paper describes an origami active learning exercise to teach the principles of Scrum in management information systems courses. The exercise shows students how Agile methods respond to changes in requirements during project…

Developing Preservice Elementary Teachers' Knowledge and Practices through Modeling-Centered Scientific Inquiry

ERIC Educational Resources Information Center

Schwarz, Christina

2009-01-01

Preservice elementary teachers face many challenges in learning how to teach science effectively, such as engaging students in science, organizing instruction, and developing a productive learning community. This paper reports on several iterative cycles of design-based research aimed at fostering preservice teachers' principled reasoning around…

Getting Results: Small Changes, Big Cohorts and Technology

ERIC Educational Resources Information Center

Kenney, Jacqueline L.

2012-01-01

This paper presents an example of constructive alignment in practice. Integrated technology supports were deployed to increase the consistency between learning objectives, activities and assessment and to foster student-centred, higher-order learning processes in the unit. Modifications took place over nine iterations of a second-year Marketing…

LATUX: An Iterative Workflow for Designing, Validating, and Deploying Learning Analytics Visualizations

ERIC Educational Resources Information Center

Martinez-Maldonado, Roberto; Pardo, Abelardo; Mirriahi, Negin; Yacef, Kalina; Kay, Judy; Clayphan, Andrew

2015-01-01

Designing, validating, and deploying learning analytics tools for instructors or students is a challenge that requires techniques and methods from different disciplines, such as software engineering, human-computer interaction, computer graphics, educational design, and psychology. Whilst each has established its own design methodologies, we now…

Quantum learning of classical stochastic processes: The completely positive realization problem

NASA Astrophysics Data System (ADS)

Monràs, Alex; Winter, Andreas

2016-01-01

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651-664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece in the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [M. Guţă, Phys. Rev. A 83(6), 062324 (2011); M. Guţă and N. Yamamoto, e-print arXiv:1303.3771(2013)].

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

Locally optimal control under unknown dynamics with learnt cost function: application to industrial robot positioning

NASA Astrophysics Data System (ADS)

Guérin, Joris; Gibaru, Olivier; Thiery, Stéphane; Nyiri, Eric

2017-01-01

Recent methods of Reinforcement Learning have enabled to solve difficult, high dimensional, robotic tasks under unknown dynamics using iterative Linear Quadratic Gaussian control theory. These algorithms are based on building a local time-varying linear model of the dynamics from data gathered through interaction with the environment. In such tasks, the cost function is often expressed directly in terms of the state and control variables so that it can be locally quadratized to run the algorithm. If the cost is expressed in terms of other variables, a model is required to compute the cost function from the variables manipulated. We propose a method to learn the cost function directly from the data, in the same way as for the dynamics. This way, the cost function can be defined in terms of any measurable quantity and thus can be chosen more appropriately for the task to be carried out. With our method, any sensor information can be used to design the cost function. We demonstrate the efficiency of this method through simulating, with the V-REP software, the learning of a Cartesian positioning task on several industrial robots with different characteristics. The robots are controlled in joint space and no model is provided a priori. Our results are compared with another model free technique, consisting in writing the cost function as a state variable.

Simulation Learning PC Screen-Based vs. High Fidelity

DTIC Science & Technology

2011-08-01

D., Burgess, L., Berg, B . and Connolly, K . (2009). Teaching mass casualty triage skills using iterative multimanikin simulations. Prehospital...SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a. REPORT U b . ABSTRACT U...learning PC screen-based vs. high fidelity – progress chart Attachment B . Approved Protocol - Simulation Learning: PC-Screen Based (PCSB) versus High

Scenario-based fitted Q-iteration for adaptive control of water reservoir systems under uncertainty

NASA Astrophysics Data System (ADS)

Bertoni, Federica; Giuliani, Matteo; Castelletti, Andrea

2017-04-01

Over recent years, mathematical models have largely been used to support planning and management of water resources systems. Yet, the increasing uncertainties in their inputs - due to increased variability in the hydrological regimes - are a major challenge to the optimal operations of these systems. Such uncertainty, boosted by projected changing climate, violates the stationarity principle generally used for describing hydro-meteorological processes, which assumes time persisting statistical characteristics of a given variable as inferred by historical data. As this principle is unlikely to be valid in the future, the probability density function used for modeling stochastic disturbances (e.g., inflows) becomes an additional uncertain parameter of the problem, which can be described in a deterministic and set-membership based fashion. This study contributes a novel method for designing optimal, adaptive policies for controlling water reservoir systems under climate-related uncertainty. The proposed method, called scenario-based Fitted Q-Iteration (sFQI), extends the original Fitted Q-Iteration algorithm by enlarging the state space to include the space of the uncertain system's parameters (i.e., the uncertain climate scenarios). As a result, sFQI embeds the set-membership uncertainty of the future inflow scenarios in the action-value function and is able to approximate, with a single learning process, the optimal control policy associated to any scenario included in the uncertainty set. The method is demonstrated on a synthetic water system, consisting of a regulated lake operated for ensuring reliable water supply to downstream users. Numerical results show that the sFQI algorithm successfully identifies adaptive solutions to operate the system under different inflow scenarios, which outperform the control policy designed under historical conditions. Moreover, the sFQI policy generalizes over inflow scenarios not directly experienced during the policy design, thus alleviating the risk of mis-adaptation, namely the design of a solution fully adapted to a scenario that is different from the one that will actually realize.

Conceptual Design of the ITER Plasma Control System

NASA Astrophysics Data System (ADS)

Snipes, J. A.

2013-10-01

The conceptual design of the ITER Plasma Control System (PCS) has been approved and the preliminary design has begun for the 1st plasma PCS. This is a collaboration of many plasma control experts from existing devices to design and test plasma control techniques applicable to ITER on existing machines. The conceptual design considered all phases of plasma operation, ranging from non-active H/He plasmas through high fusion gain inductive DT plasmas to fully non-inductive steady-state operation, to ensure that the PCS control functionality and architecture can satisfy the demands of the ITER Research Plan. The PCS will control plasma equilibrium and density, plasma heat exhaust, a range of MHD instabilities (including disruption mitigation), and the non-inductive current profile required to maintain stable steady-state scenarios. The PCS architecture requires sophisticated shared actuator management and event handling systems to prioritize control goals, algorithms, and actuators according to dynamic control needs and monitor plasma and plant system events to trigger automatic changes in the control algorithms or operational scenario, depending on real-time operating limits and conditions.

Design Approaches to Support Preservice Teachers in Scientific Modeling

NASA Astrophysics Data System (ADS)

Kenyon, Lisa; Davis, Elizabeth A.; Hug, Barbara

2011-02-01

Engaging children in scientific practices is hard for beginning teachers. One such scientific practice with which beginning teachers may have limited experience is scientific modeling. We have iteratively designed preservice teacher learning experiences and materials intended to help teachers achieve learning goals associated with scientific modeling. Our work has taken place across multiple years at three university sites, with preservice teachers focused on early childhood, elementary, and middle school teaching. Based on results from our empirical studies supporting these design decisions, we discuss design features of our modeling instruction in each iteration. Our results suggest some successes in supporting preservice teachers in engaging students in modeling practice. We propose design principles that can guide science teacher educators in incorporating modeling in teacher education.

A Randomized Crossover Design to Assess Learning Impact and Student Preference for Active and Passive Online Learning Modules.

PubMed

Prunuske, Amy J; Henn, Lisa; Brearley, Ann M; Prunuske, Jacob

Medical education increasingly involves online learning experiences to facilitate the standardization of curriculum across time and space. In class, delivering material by lecture is less effective at promoting student learning than engaging students in active learning experience and it is unclear whether this difference also exists online. We sought to evaluate medical student preferences for online lecture or online active learning formats and the impact of format on short- and long-term learning gains. Students participated online in either lecture or constructivist learning activities in a first year neurologic sciences course at a US medical school. In 2012, students selected which format to complete and in 2013, students were randomly assigned in a crossover fashion to the modules. In the first iteration, students strongly preferred the lecture modules and valued being told "what they need to know" rather than figuring it out independently. In the crossover iteration, learning gains and knowledge retention were found to be equivalent regardless of format, and students uniformly demonstrated a strong preference for the lecture format, which also on average took less time to complete. When given a choice for online modules, students prefer passive lecture rather than completing constructivist activities, and in the time-limited environment of medical school, this choice results in similar performance on multiple-choice examinations with less time invested. Instructors need to look more carefully at whether assessments and learning strategies are helping students to obtain self-directed learning skills and to consider strategies to help students learn to value active learning in an online environment.

Preliminary consideration of CFETR ITER-like case diagnostic system.

PubMed

Li, G S; Yang, Y; Wang, Y M; Ming, T F; Han, X; Liu, S C; Wang, E H; Liu, Y K; Yang, W J; Li, G Q; Hu, Q S; Gao, X

2016-11-01

Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basic control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.

Preliminary consideration of CFETR ITER-like case diagnostic system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, G. S.; Liu, Y. K.; Gao, X.

2016-11-15

Chinese Fusion Engineering Test Reactor (CFETR) is a new superconducting tokamak device being designed in China, which aims at bridging the gap between ITER and DEMO, where DEMO is a tokamak demonstration fusion reactor. Two diagnostic cases, ITER-like case and towards DEMO case, have been considered for CFETR early and later operating phases, respectively. In this paper, some preliminary consideration of ITER-like case will be presented. Based on ITER diagnostic system, three versions of increased complexity and coverage of the ITER-like case diagnostic system have been developed with different goals and functions. Version A aims only machine protection and basicmore » control. Both of version B and version C are mainly for machine protection, basic and advanced control, but version C has an increased level of redundancy necessary for improved measurements capability. The performance of these versions and needed R&D work are outlined.« less

Diverse power iteration embeddings: Theory and practice

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-11-09

Manifold learning, especially spectral embedding, is known as one of the most effective learning approaches on high dimensional data, but for real-world applications it raises a serious computational burden in constructing spectral embeddings for large datasets. To overcome this computational complexity, we propose a novel efficient embedding construction, Diverse Power Iteration Embedding (DPIE). DPIE shows almost the same effectiveness of spectral embeddings and yet is three order of magnitude faster than spectral embeddings computed from eigen-decomposition. Our DPIE is unique in that (1) it finds linearly independent embeddings and thus shows diverse aspects of dataset; (2) the proposed regularized DPIEmore » is effective if we need many embeddings; (3) we show how to efficiently orthogonalize DPIE if one needs; and (4) Diverse Power Iteration Value (DPIV) provides the importance of each DPIE like an eigen value. As a result, such various aspects of DPIE and DPIV ensure that our algorithm is easy to apply to various applications, and we also show the effectiveness and efficiency of DPIE on clustering, anomaly detection, and feature selection as our case studies.« less

Clinical Training at Remote Sites Using Mobile Technology: An India-USA Partnership

ERIC Educational Resources Information Center

Vyas, R.; Albright, S.; Walker, D.; Zachariah, A.; Lee, M. Y.

2010-01-01

Christian Medical College (CMC), India, and Tufts University School of Medicine, USA, have developed an "institutional hub and spokes" model (campus-based e-learning supporting m-learning in the field) to facilitate clinical education and training at remote secondary hospital sites across India. Iterative research, design, development,…

Explaining Cooperation in Groups: Testing Models of Reciprocity and Learning

ERIC Educational Resources Information Center

Biele, Guido; Rieskamp, Jorg; Czienskowski, Uwe

2008-01-01

What are the cognitive processes underlying cooperation in groups? This question is addressed by examining how well a reciprocity model, two learning models, and social value orientation can predict cooperation in two iterated n-person social dilemmas with continuous contributions. In the first of these dilemmas, the public goods game,…

Modeling Peer Assessment as Agent Negotiation in a Computer Supported Collaborative Learning Environment

ERIC Educational Resources Information Center

Lai, K. Robert; Lan, Chung Hsien

2006-01-01

This work presents a novel method for modeling collaborative learning as multi-issue agent negotiation using fuzzy constraints. Agent negotiation is an iterative process, through which, the proposed method aggregates student marks to reduce personal bias. In the framework, students define individual fuzzy membership functions based on their…

Who Are with Us: MOOC Learners on a FutureLearn Course

ERIC Educational Resources Information Center

Liyanagunawardena, Tharindu Rekha; Lundqvist, Karsten Øster; Williams, Shirley Ann

2015-01-01

Massive open online courses (MOOCs) attract learners with a variety of backgrounds. Engaging them using game development was trialled in a beginner's programming course, "Begin programming: build your first mobile game," on FutureLearn platform. The course has completed two iterations: first in autumn 2013 and second in spring 2014 with…

Rules, Roles and Tools: Activity Theory and the Comparative Study of E-Learning

ERIC Educational Resources Information Center

Benson, Angela; Lawler, Cormac; Whitworth, Andrew

2008-01-01

Activity theory (AT) is a powerful tool for investigating "artefacts in use", ie, the ways technologies interrelate with their local context. AT reveals the interfaces between e-learning at the macro- (strategy, policy, "campus-wide" solutions) and the micro-organisational levels (everyday working practice, iterative change, individual…

Design Principles for "Thriving in Our Digital World": A High School Computer Science Course

ERIC Educational Resources Information Center

Veletsianos, George; Beth, Bradley; Lin, Calvin; Russell, Gregory

2016-01-01

"Thriving in Our Digital World" is a technology-enhanced dual enrollment course introducing high school students to computer science through project- and problem-based learning. This article describes the evolution of the course and five lessons learned during the design, development, implementation, and iteration of the course from its…

Multimodal and Adaptive Learning Management: An Iterative Design

ERIC Educational Resources Information Center

Squires, David R.; Orey, Michael A.

2015-01-01

The purpose of this study is to measure the outcome of a comprehensive learning management system implemented at a Spinal Cord Injury (SCI) hospital in the Southeast United States. Specifically this SCI hospital has been experiencing an evident volume of patients returning seeking more information about the nature of their injuries. Recognizing…

Is This a Meaningful Learning Experience? Interactive Critical Self-Inquiry as Investigation

ERIC Educational Resources Information Center

Allard, Andrea C.; Gallant, Andrea

2012-01-01

What conditions enable educators to engage in meaningful learning experiences with peers and beginning practitioners? This article documents a self-study on our actions-in-practice in a peer mentoring project. The investigation involved an iterative process to improve our knowledge as teacher educators, reflective practitioners, and researchers.…

Developing a Multi-Year Learning Progression for Carbon Cycling in Socio-Ecological Systems

ERIC Educational Resources Information Center

Mohan, Lindsey; Chen, Jing; Anderson, Charles W.

2009-01-01

This study reports on our steps toward achieving a conceptually coherent and empirically validated learning progression for carbon cycling in socio-ecological systems. It describes an iterative process of designing and analyzing assessment and interview data from students in upper elementary through high school. The product of our development…

User Acceptance of a Haptic Interface for Learning Anatomy

ERIC Educational Resources Information Center

Yeom, Soonja; Choi-Lundberg, Derek; Fluck, Andrew; Sale, Arthur

2013-01-01

Visualizing the structure and relationships in three dimensions (3D) of organs is a challenge for students of anatomy. To provide an alternative way of learning anatomy engaging multiple senses, we are developing a force-feedback (haptic) interface for manipulation of 3D virtual organs, using design research methodology, with iterations of system…

Challenges and Opportunities for Teacher Professional Development in Interactive Use of Technology in African Schools

ERIC Educational Resources Information Center

Hennessy, Sara; Haßler, Bjoern; Hofmann, Riikka

2015-01-01

This article examines the supporting and constraining factors influencing professional learning about interactive teaching and mobile digital technology use in low-resourced basic schools in sub-Saharan Africa. It draws on a case study of iterative development and refinement of a school-based, peer-facilitated professional learning programme…

Detangling the Interrelationships between Self- Regulation and Ill-Structured Problem Solving in Problem-Based Learning

ERIC Educational Resources Information Center

Ge, Xun; Law, Victor; Huang, Kun

2016-01-01

One of the goals for problem-based learning (PBL) is to promote self-regulation. Although self-regulation has been studied extensively, its interrelationships with ill-structured problem solving have been unclear. In order to clarify the interrelationships, this article proposes a conceptual framework illustrating the iterative processes among…

Students' Socio-Scientific Reasoning in an Astrobiological Context during Work with a Digital Learning Environment

ERIC Educational Resources Information Center

Hansson, Lena; Redfors, Andreas; Rosberg, Maria

2011-01-01

In a European project--CoReflect--researchers in seven countries are developing, implementing and evaluating teaching sequences using a web-based platform (STOCHASMOS). The interactive web-based inquiry materials support collaborative and reflective work. The learning environments will be iteratively tested and refined, during different phases of…

Learning Biological Networks via Bootstrapping with Optimized GO-based Gene Similarity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2010-08-02

Microarray gene expression data provide a unique information resource for learning biological networks using "reverse engineering" methods. However, there are a variety of cases in which we know which genes are involved in a given pathology of interest, but we do not have enough experimental evidence to support the use of fully-supervised/reverse-engineering learning methods. In this paper, we explore a novel semi-supervised approach in which biological networks are learned from a reference list of genes and a partial set of links for these genes extracted automatically from PubMed abstracts, using a knowledge-driven bootstrapping algorithm. We show how new relevant linksmore » across genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. We describe an application of this approach to the TGFB pathway as a case study and show how the ensuing results prove the feasibility of the approach as an alternate or complementary technique to fully supervised methods.« less

Multi-machine analysis of termination scenarios with comparison to simulations of controlled shutdown of ITER discharges

DOE PAGES

de Vries, Peter C.; Luce, Timothy C.; Bae, Young-soon; ...

2017-11-22

To improve our understanding of the dynamics and control of ITER terminations, a study has been carried out on data from existing tokamaks. The aim of this joint analysis is to compare the assumptions for ITER terminations with the present experience basis. The study examined the parameter ranges in which present day devices operated during their terminations, as well as the dynamics of these parameters. The analysis of a database, built using a selected set of experimental termination cases, showed that, the H-mode density decays slower than the plasma current ramp-down. The consequential increase in f_GW limits the duration ofmore » the H-mode phase or result in disruptions. The lower temperatures after the drop out of H-mode will allow the plasma internal inductance to increase. But vertical stability control remains manageable in ITER at high internal inductance when accompanied by a strong elongation reduction. This will result in ITER terminations remaining longer at low q (q₉₅~3) than most present-day devices during the current ramp-down. A fast power ramp-down leads to a larger change in β_p at the H-L transition, but the experimental data showed that these are manageable for the ITER radial position control. The analysis of JET data shows that radiation and impurity levels significantly alter the H-L transition dynamics. Self-consistent calculations of the impurity content and resulting radiation should be taken into account when modelling ITER termination scenarios. Here, the results from this analysis can be used to better prescribe the inputs for the detailed modelling and preparation of ITER termination scenarios.« less

Multi-machine analysis of termination scenarios with comparison to simulations of controlled shutdown of ITER discharges

NASA Astrophysics Data System (ADS)

de Vries, P. C.; Luce, T. C.; Bae, Y. S.; Gerhardt, S.; Gong, X.; Gribov, Y.; Humphreys, D.; Kavin, A.; Khayrutdinov, R. R.; Kessel, C.; Kim, S. H.; Loarte, A.; Lukash, V. E.; de la Luna, E.; Nunes, I.; Poli, F.; Qian, J.; Reinke, M.; Sauter, O.; Sips, A. C. C.; Snipes, J. A.; Stober, J.; Treutterer, W.; Teplukhina, A. A.; Voitsekhovitch, I.; Woo, M. H.; Wolfe, S.; Zabeo, L.; the Alcator C-MOD Team; the ASDEX Upgrade Team; the DIII-D Team; the EAST Team; contributors, JET; the KSTAR Team; the NSTX-U Team; the TCV Team; IOS members, ITPA; experts

2018-02-01

To improve our understanding of the dynamics and control of ITER terminations, a study has been carried out on data from existing tokamaks. The aim of this joint analysis is to compare the assumptions for ITER terminations with the present experience basis. The study examined the parameter ranges in which present day devices operated during their terminations, as well as the dynamics of these parameters. The analysis of a database, built using a selected set of experimental termination cases, showed that, the H-mode density decays slower than the plasma current ramp-down. The consequential increase in f GW limits the duration of the H-mode phase or result in disruptions. The lower temperatures after the drop out of H-mode will allow the plasma internal inductance to increase. But vertical stability control remains manageable in ITER at high internal inductance when accompanied by a strong elongation reduction. This will result in ITER terminations remaining longer at low q (q 95 ~ 3) than most present-day devices during the current ramp-down. A fast power ramp-down leads to a larger change in β p at the H-L transition, but the experimental data showed that these are manageable for the ITER radial position control. The analysis of JET data shows that radiation and impurity levels significantly alter the H-L transition dynamics. Self-consistent calculations of the impurity content and resulting radiation should be taken into account when modelling ITER termination scenarios. The results from this analysis can be used to better prescribe the inputs for the detailed modelling and preparation of ITER termination scenarios.

Implementation of a Curriculum-Integrated Computer Game for Introducing Scientific Argumentation

NASA Astrophysics Data System (ADS)

Wallon, Robert C.; Jasti, Chandana; Lauren, Hillary Z. G.; Hug, Barbara

2017-11-01

Argumentation has been emphasized in recent US science education reform efforts (NGSS Lead States 2013; NRC 2012), and while existing studies have investigated approaches to introducing and supporting argumentation (e.g., McNeill and Krajcik in Journal of Research in Science Teaching, 45(1), 53-78, 2008; Kang et al. in Science Education, 98(4), 674-704, 2014), few studies have investigated how game-based approaches may be used to introduce argumentation to students. In this paper, we report findings from a design-based study of a teacher's use of a computer game intended to introduce the claim, evidence, reasoning (CER) framework (McNeill and Krajcik 2012) for scientific argumentation. We studied the implementation of the game over two iterations of development in a high school biology teacher's classes. The results of this study include aspects of enactment of the activities and student argument scores. We found the teacher used the game in aspects of explicit instruction of argumentation during both iterations, although the ways in which the game was used differed. Also, students' scores in the second iteration were significantly higher than the first iteration. These findings support the notion that students can learn argumentation through a game, especially when used in conjunction with explicit instruction and support in student materials. These findings also highlight the importance of analyzing classroom implementation in studies of game-based learning.

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs.

PubMed

Aslam, Muhammad; Hu, Xiaopeng; Wang, Fan

2017-12-13

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR's routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability period when compared to existing routing protocols.

SACFIR: SDN-Based Application-Aware Centralized Adaptive Flow Iterative Reconfiguring Routing Protocol for WSNs

PubMed Central

Hu, Xiaopeng; Wang, Fan

2017-01-01

Smart reconfiguration of a dynamic networking environment is offered by the central control of Software-Defined Networking (SDN). Centralized SDN-based management architectures are capable of retrieving global topology intelligence and decoupling the forwarding plane from the control plane. Routing protocols developed for conventional Wireless Sensor Networks (WSNs) utilize limited iterative reconfiguration methods to optimize environmental reporting. However, the challenging networking scenarios of WSNs involve a performance overhead due to constant periodic iterative reconfigurations. In this paper, we propose the SDN-based Application-aware Centralized adaptive Flow Iterative Reconfiguring (SACFIR) routing protocol with the centralized SDN iterative solver controller to maintain the load-balancing between flow reconfigurations and flow allocation cost. The proposed SACFIR’s routing protocol offers a unique iterative path-selection algorithm, which initially computes suitable clustering based on residual resources at the control layer and then implements application-aware threshold-based multi-hop report transmissions on the forwarding plane. The operation of the SACFIR algorithm is centrally supervised by the SDN controller residing at the Base Station (BS). This paper extends SACFIR to SDN-based Application-aware Main-value Centralized adaptive Flow Iterative Reconfiguring (SAMCFIR) to establish both proactive and reactive reporting. The SAMCFIR transmission phase enables sensor nodes to trigger direct transmissions for main-value reports, while in the case of SACFIR, all reports follow computed routes. Our SDN-enabled proposed models adjust the reconfiguration period according to the traffic burden on sensor nodes, which results in heterogeneity awareness, load-balancing and application-specific reconfigurations of WSNs. Extensive experimental simulation-based results show that SACFIR and SAMCFIR yield the maximum scalability, network lifetime and stability period when compared to existing routing protocols. PMID:29236031

Zero-sum two-player game theoretic formulation of affine nonlinear discrete-time systems using neural networks.

PubMed

Mehraeen, Shahab; Dierks, Travis; Jagannathan, S; Crow, Mariesa L

2013-12-01

In this paper, the nearly optimal solution for discrete-time (DT) affine nonlinear control systems in the presence of partially unknown internal system dynamics and disturbances is considered. The approach is based on successive approximate solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in optimal control. Successive approximation approach for updating control and disturbance inputs for DT nonlinear affine systems are proposed. Moreover, sufficient conditions for the convergence of the approximate HJI solution to the saddle point are derived, and an iterative approach to approximate the HJI equation using a neural network (NN) is presented. Then, the requirement of full knowledge of the internal dynamics of the nonlinear DT system is relaxed by using a second NN online approximator. The result is a closed-loop optimal NN controller via offline learning. A numerical example is provided illustrating the effectiveness of the approach.

A Cost-Effectiveness Analysis of Blended Versus Face-to-Face Delivery of Evidence-Based Medicine to Medical Students

PubMed Central

Nicklen, Peter; Rivers, George; Foo, Jonathan; Ooi, Ying Ying; Reeves, Scott; Walsh, Kieran; Ilic, Dragan

2015-01-01

Background Blended learning describes a combination of teaching methods, often utilizing digital technologies. Research suggests that learner outcomes can be improved through some blended learning formats. However, the cost-effectiveness of delivering blended learning is unclear. Objective This study aimed to determine the cost-effectiveness of a face-to-face learning and blended learning approach for evidence-based medicine training within a medical program. Methods The economic evaluation was conducted as part of a randomized controlled trial (RCT) comparing the evidence-based medicine (EBM) competency of medical students who participated in two different modes of education delivery. In the traditional face-to-face method, students received ten 2-hour classes. In the blended learning approach, students received the same total face-to-face hours but with different activities and additional online and mobile learning. Online activities utilized YouTube and a library guide indexing electronic databases, guides, and books. Mobile learning involved self-directed interactions with patients in their regular clinical placements. The attribution and differentiation of costs between the interventions within the RCT was measured in conjunction with measured outcomes of effectiveness. An incremental cost-effectiveness ratio was calculated comparing the ongoing operation costs of each method with the level of EBM proficiency achieved. Present value analysis was used to calculate the break-even point considering the transition cost and the difference in ongoing operation cost. Results The incremental cost-effectiveness ratio indicated that it costs 24% less to educate a student to the same level of EBM competency via the blended learning approach used in the study, when excluding transition costs. The sunk cost of approximately AUD $40,000 to transition to the blended model exceeds any savings from using the approach within the first year of its implementation; however, a break-even point is achieved within its third iteration and relative savings in the subsequent years. The sensitivity analysis indicates that approaches with higher transition costs, or staffing requirements over that of a traditional method, are likely to result in negative value propositions. Conclusions Under the study conditions, a blended learning approach was more cost-effective to operate and resulted in improved value for the institution after the third year iteration, when compared to the traditional face-to-face model. The wider applicability of the findings are dependent on the type of blended learning utilized, staffing expertise, and educational context. PMID:26197801

Multi-criteria Integrated Resource Assessment (MIRA)

EPA Pesticide Factsheets

MIRA is an approach that facilitates stakeholder engagement for collaborative multi-objective decision making. MIRA is designed to facilitate and support an inclusive, explicit, transparent, iterative learning-based decision process.

Nonlinear Motion Tracking by Deep Learning Architecture

NASA Astrophysics Data System (ADS)

Verma, Arnav; Samaiya, Devesh; Gupta, Karunesh K.

2018-03-01

In the world of Artificial Intelligence, object motion tracking is one of the major problems. The extensive research is being carried out to track people in crowd. This paper presents a unique technique for nonlinear motion tracking in the absence of prior knowledge of nature of nonlinear path that the object being tracked may follow. We achieve this by first obtaining the centroid of the object and then using the centroid as the current example for a recurrent neural network trained using real-time recurrent learning. We have tweaked the standard algorithm slightly and have accumulated the gradient for few previous iterations instead of using just the current iteration as is the norm. We show that for a single object, such a recurrent neural network is highly capable of approximating the nonlinearity of its path.

Iterative quantization: a Procrustean approach to learning binary codes for large-scale image retrieval.

PubMed

Gong, Yunchao; Lazebnik, Svetlana; Gordo, Albert; Perronnin, Florent

2013-12-01

This paper addresses the problem of learning similarity-preserving binary codes for efficient similarity search in large-scale image collections. We formulate this problem in terms of finding a rotation of zero-centered data so as to minimize the quantization error of mapping this data to the vertices of a zero-centered binary hypercube, and propose a simple and efficient alternating minimization algorithm to accomplish this task. This algorithm, dubbed iterative quantization (ITQ), has connections to multiclass spectral clustering and to the orthogonal Procrustes problem, and it can be used both with unsupervised data embeddings such as PCA and supervised embeddings such as canonical correlation analysis (CCA). The resulting binary codes significantly outperform several other state-of-the-art methods. We also show that further performance improvements can result from transforming the data with a nonlinear kernel mapping prior to PCA or CCA. Finally, we demonstrate an application of ITQ to learning binary attributes or "classemes" on the ImageNet data set.

Advanced Data Acquisition System Implementation for the ITER Neutron Diagnostic Use Case Using EPICS and FlexRIO Technology on a PXIe Platform

NASA Astrophysics Data System (ADS)

Sanz, D.; Ruiz, M.; Castro, R.; Vega, J.; Afif, M.; Monroe, M.; Simrock, S.; Debelle, T.; Marawar, R.; Glass, B.

2016-04-01

To aid in assessing the functional performance of ITER, Fission Chambers (FC) based on the neutron diagnostic use case deliver timestamped measurements of neutron source strength and fusion power. To demonstrate the Plant System Instrumentation & Control (I&C) required for such a system, ITER Organization (IO) has developed a neutron diagnostics use case that fully complies with guidelines presented in the Plant Control Design Handbook (PCDH). The implementation presented in this paper has been developed on the PXI Express (PXIe) platform using products from the ITER catalog of standard I&C hardware for fast controllers. Using FlexRIO technology, detector signals are acquired at 125 MS/s, while filtering, decimation, and three methods of neutron counting are performed in real-time via the onboard Field Programmable Gate Array (FPGA). Measurement results are reported every 1 ms through Experimental Physics and Industrial Control System (EPICS) Channel Access (CA), with real-time timestamps derived from the ITER Timing Communication Network (TCN) based on IEEE 1588-2008. Furthermore, in accordance with ITER specifications for CODAC Core System (CCS) application development, the software responsible for the management, configuration, and monitoring of system devices has been developed in compliance with a new EPICS module called Nominal Device Support (NDS) and RIO/FlexRIO design methodology.

Convergence of Proximal Iteratively Reweighted Nuclear Norm Algorithm for Image Processing.

PubMed

Sun, Tao; Jiang, Hao; Cheng, Lizhi

2017-08-25

The nonsmooth and nonconvex regularization has many applications in imaging science and machine learning research due to its excellent recovery performance. A proximal iteratively reweighted nuclear norm algorithm has been proposed for the nonsmooth and nonconvex matrix minimizations. In this paper, we aim to investigate the convergence of the algorithm. With the Kurdyka-Łojasiewicz property, we prove the algorithm globally converges to a critical point of the objective function. The numerical results presented in this paper coincide with our theoretical findings.

Using Iterative Plan-Do-Study-Act Cycles to Improve Teaching Pedagogy.

PubMed

Murray, Elizabeth J

2018-01-15

Most students entering nursing programs today are members of Generation Y or the Millennial generation, and they learn differently than previous generations. Nurse educators must consider implementing innovative teaching strategies that appeal to the newest generation of learners. The Plan-Do-Study-Act cycle is a framework that can be helpful when planning, assessing, and continually improving teaching pedagogy. This article describes the use of iterative Plan-Do-Study-Act cycles to implement a change in teaching pedagogy.

Comparison between iterative wavefront control algorithm and direct gradient wavefront control algorithm for adaptive optics system

NASA Astrophysics Data System (ADS)

Cheng, Sheng-Yi; Liu, Wen-Jin; Chen, Shan-Qiu; Dong, Li-Zhi; Yang, Ping; Xu, Bing

2015-08-01

Among all kinds of wavefront control algorithms in adaptive optics systems, the direct gradient wavefront control algorithm is the most widespread and common method. This control algorithm obtains the actuator voltages directly from wavefront slopes through pre-measuring the relational matrix between deformable mirror actuators and Hartmann wavefront sensor with perfect real-time characteristic and stability. However, with increasing the number of sub-apertures in wavefront sensor and deformable mirror actuators of adaptive optics systems, the matrix operation in direct gradient algorithm takes too much time, which becomes a major factor influencing control effect of adaptive optics systems. In this paper we apply an iterative wavefront control algorithm to high-resolution adaptive optics systems, in which the voltages of each actuator are obtained through iteration arithmetic, which gains great advantage in calculation and storage. For AO system with thousands of actuators, the computational complexity estimate is about O(n2) ˜ O(n3) in direct gradient wavefront control algorithm, while the computational complexity estimate in iterative wavefront control algorithm is about O(n) ˜ (O(n)3/2), in which n is the number of actuators of AO system. And the more the numbers of sub-apertures and deformable mirror actuators, the more significant advantage the iterative wavefront control algorithm exhibits. Project supported by the National Key Scientific and Research Equipment Development Project of China (Grant No. ZDYZ2013-2), the National Natural Science Foundation of China (Grant No. 11173008), and the Sichuan Provincial Outstanding Youth Academic Technology Leaders Program, China (Grant No. 2012JQ0012).

Using Analytics to Transform a Problem-Based Case Library: An Educational Design Research Approach

ERIC Educational Resources Information Center

Schmidt, Matthew; Tawfik, Andrew A.

2018-01-01

This article describes the iterative design, development, and evaluation of a case-based learning environment focusing on an ill-structured sales management problem. We discuss our processes and situate them within the broader framework of educational design research. The learning environment evolved over the course of three design phases. A…

Exploring the Impact of Global Studies Experiences on Undergraduate Student Development: Some Curricular Considerations

ERIC Educational Resources Information Center

Stephens, Christopher J.; Morford, Z. Harrison; Cihon, Traci M.; Forand, Elissa Hamilton; Neri-Hernández, Lucero

2018-01-01

In this manuscript, the authors detail the initial evaluations of the effects of participation in two iterations of an interdisciplinary learning community with a short-term study abroad opportunity on undergraduate student learning and behavior. The results suggest that the CHE (Cultural, Historical, and Environmental) log tool may be useful in…

Design and Facilitation of Problem-Based Learning in Graduate Teacher Education: An MA TESOL Case

ERIC Educational Resources Information Center

Caswell, Cynthia Ann

2016-01-01

This exploratory, evaluative case study introduces a new context for problem-based learning (PBL) involving an iterative, modular approach to curriculum-wide delivery of PBL in an MA TESOL program. The introduction to the curriculum context provides an overview of the design and delivery features particular to the situation. The delivery approach…

Using Learning Trajectories for Teacher Learning to Structure Professional Development

ERIC Educational Resources Information Center

Bargagliotti, Anna E.; Anderson, Celia Rousseau

2017-01-01

As a result of the increased focus on data literacy and data science across the world, there has been a large demand for professional development in statistics. However, exactly how these professional development opportunities should be structured remains an open question. The purpose of this paper is to describe the first iteration of a design…

Shaping Aspirations, Awareness, Academics, and Action: Outcomes of Summer Enrichment Programs for English-Learning Secondary Students

ERIC Educational Resources Information Center

Matthews, Paul H.; Mellom, Paula J.

2012-01-01

Mixed-method evaluation of two iterations of month-long summer enrichment programs for English-learning secondary students investigated impacts on participants' beliefs about school and academic achievement, and on actual course choices, test outcomes, and graduation rates. Students (N = 85) from one ethnically diverse, high-poverty high school in…

Introducing 12 Year-Olds to Elementary Particles

ERIC Educational Resources Information Center

Wiener, Gerfried J.; Schmeling, Sascha M.; Hopf, Martin

2017-01-01

We present a new learning unit, which introduces 12 year-olds to the subatomic structure of matter. The learning unit was iteratively developed as a design-based research project using the technique of probing acceptance. We give a brief overview of the unit's final version, discuss its key ideas and main concepts, and conclude by highlighting the…

The Iterative Design of a Mobile Learning Application to Support Scientific Inquiry

ERIC Educational Resources Information Center

Marty, Paul F.; Mendenhall, Anne; Douglas, Ian; Southerland, Sherry A.; Sampson, Victor; Kazmer, Michelle M.; Alemanne, Nicole; Clark, Amanda; Schellinger, Jennifer

2013-01-01

The ubiquity of mobile devices makes them well suited for field-based learning experiences that require students to gather data as part of the process of developing scientific inquiry practices. The usefulness of these devices, however, is strongly influenced by the nature of the applications students use to collect data in the field. To…

A policy iteration approach to online optimal control of continuous-time constrained-input systems.

PubMed

Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L

2013-09-01

This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. Copyright © 2013 ISA. All rights reserved.

A Tale of Two Chambers: Iterative Approaches and Lessons Learned from Life Support Systems Testing in Altitude Chambers

NASA Technical Reports Server (NTRS)

Callini, Gianluca

2016-01-01

The drive for the journey to Mars is in a higher gear than ever before. We are developing new spacecraft and life support systems to take humans to the Red Planet. The journey that development hardware takes before its final incarnation in a fully integrated spacecraft can take years, as is the case for the Orion environmental control and life support system (ECLSS). Through the Pressure Integrated Suit Test (PIST) series, NASA personnel at Johnson Space Center have been characterizing the behavior of a closed loop ECLSS in the event of cabin depressurization. This kind of testing - one of the most hazardous activities performed at JSC - requires an iterative approach, increasing in complexity and hazards). The PIST series, conducted in the Crew and Thermal Systems Division (CTSD) 11-ft Chamber, started with unmanned test precursors before moving to a human-in-the-loop phase, and continues to evolve with the eventual goal of a qualification test for the final system that will be installed on Orion. Meanwhile, the Human Exploration Spacecraft Testbed for Integration and Advancement (HESTIA) program is an effort to research and develop technologies that will work in concert to support habitation on Mars. September 2015 marked the first unmanned HESTIA test, with the goal of characterizing how ECLSS technologies work together in a closed environment. HESTIA will culminate in crewed testing, but it can benefit from the lessons learned from another test that is farther ahead in its development and life cycle. Discussing PIST and HESTIA, this paper illustrates how we approach testing, the kind of information that facility teams need to ensure efficient collaborations and successful testing, and how we can apply what we learn to execute future tests.

ELM mitigation techniques

NASA Astrophysics Data System (ADS)

Evans, T. E.

2013-07-01

Large edge-localized mode (ELM) control techniques must be developed to help ensure the success of burning and ignited fusion plasma devices such as tokamaks and stellarators. In full performance ITER tokamak discharges, with QDT = 10, the energy released by a single ELM could reach ˜30 MJ which is expected to result in an energy density of 10-15 MJ/m2on the divertor targets. This will exceed the estimated divertor ablation limit by a factor of 20-30. A worldwide research program is underway to develop various types of ELM control techniques in preparation for ITER H-mode plasma operations. An overview of the ELM control techniques currently being developed is discussed along with the requirements for applying these techniques to plasmas in ITER. Particular emphasis is given to the primary approaches, pellet pacing and resonant magnetic perturbation fields, currently being considered for ITER.

Decentralized control of sound radiation using iterative loop recovery.

PubMed

Schiller, Noah H; Cabell, Randolph H; Fuller, Chris R

2010-10-01

A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

Decentralized Control of Sound Radiation Using Iterative Loop Recovery

NASA Technical Reports Server (NTRS)

Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

2009-01-01

A decentralized model-based control strategy is designed to reduce low-frequency sound radiation from periodically stiffened panels. While decentralized control systems tend to be scalable, performance can be limited due to modeling error introduced by the unmodeled interaction between neighboring control units. Since bounds on modeling error are not known in advance, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is evaluated numerically using a model of a stiffened aluminum panel that is representative of the sidewall of an aircraft. Simulations demonstrate that the iterative approach can achieve significant reductions in radiated sound power from the stiffened panel without destabilizing neighboring control units.

Operation of large RF sources for H-: Lessons learned at ELISE

NASA Astrophysics Data System (ADS)

Fantz, U.; Wünderlich, D.; Heinemann, B.; Kraus, W.; Riedl, R.

2017-08-01

The goal of the ELISE test facility is to demonstrate that large RF-driven negative ion sources (1 × 1 m2 source area with 360 kW installed RF power) can achieve the parameters required for the ITER beam sources in terms of current densities and beam homogeneity at a filling pressure of 0.3 Pa for pulse lengths of up to one hour. With the experience in operation of the test facility, the beam source inspection and maintenance as well as with the results of the achieved source performance so far, conclusions are drawn for commissioning and operation of the ITER beam sources. Addressed are critical technical RF issues, extrapolations to the required RF power, Cs consumption and Cs ovens, the need of adjusting the magnetic filter field strength as well as the temporal dynamic and spatial asymmetry of the co-extracted electron current. It is proposed to relax the low pressure limit to 0.4 Pa and to replace the fixed electron-to-ion ratio by a power density limit for the extraction grid. This would be highly beneficial for controlling the co-extracted electrons.

Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

PubMed

Wu, Huai-Ning; Luo, Biao

2012-12-01

It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm.

Iterative LQG Controller Design Through Closed-Loop Identification

NASA Technical Reports Server (NTRS)

Hsiao, Min-Hung; Huang, Jen-Kuang; Cox, David E.

1996-01-01

This paper presents an iterative Linear Quadratic Gaussian (LQG) controller design approach for a linear stochastic system with an uncertain open-loop model and unknown noise statistics. This approach consists of closed-loop identification and controller redesign cycles. In each cycle, the closed-loop identification method is used to identify an open-loop model and a steady-state Kalman filter gain from closed-loop input/output test data obtained by using a feedback LQG controller designed from the previous cycle. Then the identified open-loop model is used to redesign the state feedback. The state feedback and the identified Kalman filter gain are used to form an updated LQC controller for the next cycle. This iterative process continues until the updated controller converges. The proposed controller design is demonstrated by numerical simulations and experiments on a highly unstable large-gap magnetic suspension system.

Mitigation of time-varying distortions in Nyquist-WDM systems using machine learning

NASA Astrophysics Data System (ADS)

Granada Torres, Jhon J.; Varughese, Siddharth; Thomas, Varghese A.; Chiuchiarelli, Andrea; Ralph, Stephen E.; Cárdenas Soto, Ana M.; Guerrero González, Neil

2017-11-01

We propose a machine learning-based nonsymmetrical demodulation technique relying on clustering to mitigate time-varying distortions derived from several impairments such as IQ imbalance, bias drift, phase noise and interchannel interference. Experimental results show that those impairments cause centroid movements in the received constellations seen in time-windows of 10k symbols in controlled scenarios. In our demodulation technique, the k-means algorithm iteratively identifies the cluster centroids in the constellation of the received symbols in short time windows by means of the optimization of decision thresholds for a minimum BER. We experimentally verified the effectiveness of this computationally efficient technique in multicarrier 16QAM Nyquist-WDM systems over 270 km links. Our nonsymmetrical demodulation technique outperforms the conventional QAM demodulation technique, reducing the OSNR requirement up to ∼0.8 dB at a BER of 1 × 10-2 for signals affected by interchannel interference.

Locomotion training of legged robots using hybrid machine learning techniques

NASA Technical Reports Server (NTRS)

Simon, William E.; Doerschuk, Peggy I.; Zhang, Wen-Ran; Li, Andrew L.

1995-01-01

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Adaptive rehabilitation gaming system: on-line individualization of stroke rehabilitation.

PubMed

Nirme, Jens; Duff, Armin; Verschure, Paul F M J

2011-01-01

The effects of stroke differ considerably in degree and symptoms for different patients. It has been shown that specific, individualized and varied therapy favors recovery. The Rehabilitation Gaming System (RGS) is a Virtual Reality (VR) based rehabilitation system designed following these principles. We have developed two algorithms to control the level of task difficulty that a user of the RGS is exposed to, as well as providing controlled variation in the therapy. In this paper, we compare the two algorithms by running numerical simulations and a study with healthy subjects. We show that both algorithms allow for individualization of the challenge level of the task. Further, the results reveal that the algorithm that iteratively learns a user model for each subject also allows a high variation of the task.

e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

NASA Astrophysics Data System (ADS)

Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

2016-02-01

XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

A Distributed Learning Method for ℓ1-Regularized Kernel Machine over Wireless Sensor Networks

PubMed Central

Ji, Xinrong; Hou, Cuiqin; Hou, Yibin; Gao, Fang; Wang, Shulong

2016-01-01

In wireless sensor networks, centralized learning methods have very high communication costs and energy consumption. These are caused by the need to transmit scattered training examples from various sensor nodes to the central fusion center where a classifier or a regression machine is trained. To reduce the communication cost, a distributed learning method for a kernel machine that incorporates ℓ1 norm regularization (ℓ1-regularized) is investigated, and a novel distributed learning algorithm for the ℓ1-regularized kernel minimum mean squared error (KMSE) machine is proposed. The proposed algorithm relies on in-network processing and a collaboration that transmits the sparse model only between single-hop neighboring nodes. This paper evaluates the proposed algorithm with respect to the prediction accuracy, the sparse rate of model, the communication cost and the number of iterations on synthetic and real datasets. The simulation results show that the proposed algorithm can obtain approximately the same prediction accuracy as that obtained by the batch learning method. Moreover, it is significantly superior in terms of the sparse rate of model and communication cost, and it can converge with fewer iterations. Finally, an experiment conducted on a wireless sensor network (WSN) test platform further shows the advantages of the proposed algorithm with respect to communication cost. PMID:27376298

User-Driven Sampling Strategies in Image Exploitation

DOE PAGES

Harvey, Neal R.; Porter, Reid B.

2013-12-23

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-drivenmore » sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. We discovered that in user-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. Furthermore, in preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.« less

User-driven sampling strategies in image exploitation

NASA Astrophysics Data System (ADS)

Harvey, Neal; Porter, Reid

2013-12-01

Visual analytics and interactive machine learning both try to leverage the complementary strengths of humans and machines to solve complex data exploitation tasks. These fields overlap most significantly when training is involved: the visualization or machine learning tool improves over time by exploiting observations of the human-computer interaction. This paper focuses on one aspect of the human-computer interaction that we call user-driven sampling strategies. Unlike relevance feedback and active learning sampling strategies, where the computer selects which data to label at each iteration, we investigate situations where the user selects which data is to be labeled at each iteration. User-driven sampling strategies can emerge in many visual analytics applications but they have not been fully developed in machine learning. User-driven sampling strategies suggest new theoretical and practical research questions for both visualization science and machine learning. In this paper we identify and quantify the potential benefits of these strategies in a practical image analysis application. We find user-driven sampling strategies can sometimes provide significant performance gains by steering tools towards local minima that have lower error than tools trained with all of the data. In preliminary experiments we find these performance gains are particularly pronounced when the user is experienced with the tool and application domain.

Online Bahavior Aquisition of an Agent based on Coaching as Learning Assistance

NASA Astrophysics Data System (ADS)

Hirokawa, Masakazu; Suzuki, Kenji

This paper describes a novel methodology, namely ``Coaching'', which allows humans to give a subjective evaluation to an agent in an iterative manner. This is an interactive learning method to improve the reinforcement learning by modifying a reward function dynamically according to given evaluations by a trainer and the learning situation of the agent. We demonstrate that the agent can learn different reward functions by given instructions such as ``good or bad'' by human's observation, and can also obtain a set of behavior based on the learnt reward functions through several experiments.

Creating a Learning Organization for State, Local, and Tribal Law Enforcement to Combat Violent Extremism

DTIC Science & Technology

2016-09-01

iterations in that time for the student practitioners to work through. When possible, case studies will be selected from actual counter-radicalizations...justify participation in the learning 9 organization. Those cases will be evaluated on a case -by- case basis and the need to expand the CVE mission...interested within the learning organization. The National Fire Academy Executive Fire Officer Program applied research pre -course is an example of

Visual recognition and inference using dynamic overcomplete sparse learning.

PubMed

Murray, Joseph F; Kreutz-Delgado, Kenneth

2007-09-01

We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter.

Exploring Alignment among Learning Progressions, Teacher-Designed Formative Assessment Tasks, and Student Growth: Results of a Four-Year Study

ERIC Educational Resources Information Center

Furtak, Erin Marie; Circi, Ruhan; Heredia, Sara C.

2018-01-01

This article describes a 4-year study of experienced high school biology teachers' participation in a five-step professional development experience in which they iteratively studied student ideas with the support of a set of learning progressions, designed formative assessment activities, practiced using those activities with their students,…

Fostering Self-Regulated Learning in a Blended Environment Using Group Awareness and Peer Assistance as External Scaffolds

ERIC Educational Resources Information Center

Lin, J-W.; Lai, Y-C.; Lai, Y-C.; Chang, L-C.

2016-01-01

Most systems for training self-regulated learning (SRL) behaviour focus on the provision of a learner-centred environment. Such systems repeat the training process and place learners alone to experience that process iteratively. According to the relevant literature, external scaffolds are more promising for effective SRL training. In this work,…

Self-Regulated Learning: The Continuous-Change Conceptual Framework and a Vision of New Paradigm, Technology System, and Pedagogical Support

ERIC Educational Resources Information Center

Huh, Yeol; Reigeluth, Charles M.

2017-01-01

A modified conceptual framework called the Continuous-Change Framework for self-regulated learning (SRL) is presented. Common elements and limitations among the past frameworks are discussed in relation to the modified conceptual framework. The iterative nature of the goal setting process and overarching presence of self-efficacy and motivational…

For the Love of Statistics: Appreciating and Learning to Apply Experimental Analysis and Statistics through Computer Programming Activities

ERIC Educational Resources Information Center

Mascaró, Maite; Sacristán, Ana Isabel; Rufino, Marta M.

2016-01-01

For the past 4 years, we have been involved in a project that aims to enhance the teaching and learning of experimental analysis and statistics, of environmental and biological sciences students, through computational programming activities (using R code). In this project, through an iterative design, we have developed sequences of R-code-based…

Playing and Learning: An iPad Game Development & Implementation Case Study

ERIC Educational Resources Information Center

Jenson, Jennifer; de Castell, Suzanne; Muehrer, Rachel; McLaughlin-Jenkins, Erin

2016-01-01

There is a great deal of enthusiasm for the use of games in formal educational contexts; however, there is a notable and problematic lack of studies that make use of replicable study designs to empirically link games to learning (Young, et al., 2012). This paper documents the iterative design and development of an educationally focused game,…

A Machine Learning System for Analyzing Human Tactics in a Game

NASA Astrophysics Data System (ADS)

Ito, Hirotaka; Tanaka, Toshimitsu; Sugie, Noboru

In order to realize advanced man-machine interfaces, it is desired to develop a system that can infer the mental state of human users and then return appropriate responses. As the first step toward the above goal, we developed a system capable of inferring human tactics in a simple game played between the system and a human. We present a machine learning system that plays a color expectation game. The system infers the tactics of the opponent, and then decides the action based on the result. We employed a modified version of classifier system like XCS in order to design the system. In addition, three methods are proposed in order to accelerate the learning rate. They are a masking method, an iterative method, and tactics templates. The results of computer experiments confirmed that the proposed methods effectively accelerate the machine learning. The masking method and the iterative method are effective to a simple strategy that considers only a part of past information. However, study speed of these methods is not enough for the tactics that refers to a lot of past information. For the case, the tactics template was able to settle the study rapidly when the tactics is identified.

Low-rank structure learning via nonconvex heuristic recovery.

PubMed

Deng, Yue; Dai, Qionghai; Liu, Risheng; Zhang, Zengke; Hu, Sanqing

2013-03-01

In this paper, we propose a nonconvex framework to learn the essential low-rank structure from corrupted data. Different from traditional approaches, which directly utilizes convex norms to measure the sparseness, our method introduces more reasonable nonconvex measurements to enhance the sparsity in both the intrinsic low-rank structure and the sparse corruptions. We will, respectively, introduce how to combine the widely used ℓp norm (0 < p < 1) and log-sum term into the framework of low-rank structure learning. Although the proposed optimization is no longer convex, it still can be effectively solved by a majorization-minimization (MM)-type algorithm, with which the nonconvex objective function is iteratively replaced by its convex surrogate and the nonconvex problem finally falls into the general framework of reweighed approaches. We prove that the MM-type algorithm can converge to a stationary point after successive iterations. The proposed model is applied to solve two typical problems: robust principal component analysis and low-rank representation. Experimental results on low-rank structure learning demonstrate that our nonconvex heuristic methods, especially the log-sum heuristic recovery algorithm, generally perform much better than the convex-norm-based method (0 < p < 1) for both data with higher rank and with denser corruptions.

Effective learning strategies for real-time image-guided adaptive control of multiple-source hyperthermia applicators.

PubMed

Cheng, Kung-Shan; Dewhirst, Mark W; Stauffer, Paul R; Das, Shiva

2010-03-01

This paper investigates overall theoretical requirements for reducing the times required for the iterative learning of a real-time image-guided adaptive control routine for multiple-source heat applicators, as used in hyperthermia and thermal ablative therapy for cancer. Methods for partial reconstruction of the physical system with and without model reduction to find solutions within a clinically practical timeframe were analyzed. A mathematical analysis based on the Fredholm alternative theorem (FAT) was used to compactly analyze the existence and uniqueness of the optimal heating vector under two fundamental situations: (1) noiseless partial reconstruction and (2) noisy partial reconstruction. These results were coupled with a method for further acceleration of the solution using virtual source (VS) model reduction. The matrix approximation theorem (MAT) was used to choose the optimal vectors spanning the reduced-order subspace to reduce the time for system reconstruction and to determine the associated approximation error. Numerical simulations of the adaptive control of hyperthermia using VS were also performed to test the predictions derived from the theoretical analysis. A thigh sarcoma patient model surrounded by a ten-antenna phased-array applicator was retained for this purpose. The impacts of the convective cooling from blood flow and the presence of sudden increase of perfusion in muscle and tumor were also simulated. By FAT, partial system reconstruction directly conducted in the full space of the physical variables such as phases and magnitudes of the heat sources cannot guarantee reconstructing the optimal system to determine the global optimal setting of the heat sources. A remedy for this limitation is to conduct the partial reconstruction within a reduced-order subspace spanned by the first few maximum eigenvectors of the true system matrix. By MAT, this VS subspace is the optimal one when the goal is to maximize the average tumor temperature. When more than 6 sources present, the steps required for a nonlinear learning scheme is theoretically fewer than that of a linear one, however, finite number of iterative corrections is necessary for a single learning step of a nonlinear algorithm. Thus, the actual computational workload for a nonlinear algorithm is not necessarily less than that required by a linear algorithm. Based on the analysis presented herein, obtaining a unique global optimal heating vector for a multiple-source applicator within the constraints of real-time clinical hyperthermia treatments and thermal ablative therapies appears attainable using partial reconstruction with minimum norm least-squares method with supplemental equations. One way to supplement equations is the inclusion of a method of model reduction.

Neural Generalized Predictive Control: A Newton-Raphson Implementation

NASA Technical Reports Server (NTRS)

Soloway, Donald; Haley, Pamela J.

1997-01-01

An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant's nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm's implementation are also included.

IMPLICIT DUAL CONTROL BASED ON PARTICLE FILTERING AND FORWARD DYNAMIC PROGRAMMING.

PubMed

Bayard, David S; Schumitzky, Alan

2010-03-01

This paper develops a sampling-based approach to implicit dual control. Implicit dual control methods synthesize stochastic control policies by systematically approximating the stochastic dynamic programming equations of Bellman, in contrast to explicit dual control methods that artificially induce probing into the control law by modifying the cost function to include a term that rewards learning. The proposed implicit dual control approach is novel in that it combines a particle filter with a policy-iteration method for forward dynamic programming. The integration of the two methods provides a complete sampling-based approach to the problem. Implementation of the approach is simplified by making use of a specific architecture denoted as an H-block. Practical suggestions are given for reducing computational loads within the H-block for real-time applications. As an example, the method is applied to the control of a stochastic pendulum model having unknown mass, length, initial position and velocity, and unknown sign of its dc gain. Simulation results indicate that active controllers based on the described method can systematically improve closed-loop performance with respect to other more common stochastic control approaches.

Low power femtosecond tip-based nanofabrication with advanced control

NASA Astrophysics Data System (ADS)

Liu, Jiangbo; Guo, Zhixiong; Zou, Qingze

2018-02-01

In this paper, we propose an approach to enable the use of low power femtosecond laser in tip-based nanofabrication (TBN) without thermal damage. One major challenge in laser-assisted TBN is in maintaining precision control of the tip-surface positioning throughout the fabrication process. An advanced iterative learning control technique is exploited to overcome this challenge in achieving high-quality patterning of arbitrary shape on a metal surface. The experimental results are analyzed to understand the ablation mechanism involved. Specifically, the near-field radiation enhancement is examined via the surface-enhanced Raman scattering effect, and it was revealed the near-field enhanced plasma-mediated ablation. Moreover, silicon nitride tip is utilized to alleviate the adverse thermal damage. Experiment results including line patterns fabricated under different writing speeds and an "R" pattern are presented. The fabrication quality with regard to the line width, depth, and uniformity is characterized to demonstrate the efficacy of the proposed approach.

2009 Space Shuttle Probabilistic Risk Assessment Overview

NASA Technical Reports Server (NTRS)

Hamlin, Teri L.; Canga, Michael A.; Boyer, Roger L.; Thigpen, Eric B.

2010-01-01

Loss of a Space Shuttle during flight has severe consequences, including loss of a significant national asset; loss of national confidence and pride; and, most importantly, loss of human life. The Shuttle Probabilistic Risk Assessment (SPRA) is used to identify risk contributors and their significance; thus, assisting management in determining how to reduce risk. In 2006, an overview of the SPRA Iteration 2.1 was presented at PSAM 8 [1]. Like all successful PRAs, the SPRA is a living PRA and has undergone revisions since PSAM 8. The latest revision to the SPRA is Iteration 3. 1, and it will not be the last as the Shuttle program progresses and more is learned. This paper discusses the SPRA scope, overall methodology, and results, as well as provides risk insights. The scope, assumptions, uncertainties, and limitations of this assessment provide risk-informed perspective to aid management s decision-making process. In addition, this paper compares the Iteration 3.1 analysis and results to the Iteration 2.1 analysis and results presented at PSAM 8.

Examination of the Entry to Burn and Burn Control for the ITER 15 MA Baseline and Other Scenarios

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kesse, Charles E.; Kim, S-H.; Koechl, F.

2014-09-01

The entry to burn and flattop burn control in ITER will be a critical need from the first DT experiments. Simulations are used to address time-dependent behavior under a range of possible conditions that include injected power level, impurity content (W, Ar, Be), density evolution, H-mode regimes, controlled parameter (Wth, Pnet, Pfusion), and actuator (Paux, fueling, fAr), with a range of transport models. A number of physics issues at the L-H transition require better understanding to project to ITER, however, simulations indicate viable control with sufficient auxiliary power (up to 73 MW), while lower powers become marginal (as low asmore » 43 MW).« less

Monitor design with multiple self-loops for maximally permissive supervisors.

PubMed

Chen, YuFeng; Li, ZhiWu; Barkaoui, Kamel; Uzam, Murat

2016-03-01

In this paper, we improve the previous work by considering that a control place can have multiple self-loops. Then, two integer linear programming problems (ILPPs) are formulated. Based on the first ILPP, an iterative deadlock control policy is developed, where a control place is computed at each iteration to implement as many marking/transition separation instances (MTSIs) as possible. The second ILPP can find a set of control places to implement all MTSIs and the objective function is used to minimize the number of control places. It is a non-iterative deadlock control strategy since we need to solve the ILPP only once. Both ILPPs can make all legal markings reachable in the controlled system, i.e., the obtained supervisor is behaviorally optimal. Finally, we provide examples to illustrate the proposed approaches. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Quantum-assisted learning of graphical models with arbitrary pairwise connectivity

NASA Astrophysics Data System (ADS)

Realpe-Gómez, John; Benedetti, Marcello; Biswas, Rupak; Perdomo-Ortiz, Alejandro

Mainstream machine learning techniques rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speedup these tasks. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful machine learning models. Here we show how to surpass this `curse of limited connectivity' bottleneck and illustrate our findings by training probabilistic generative models with arbitrary pairwise connectivity on a real dataset of handwritten digits and two synthetic datasets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding Boltzmann-like distribution. Therefore, the need to infer the effective temperature at each iteration is avoided, speeding up learning, and the effect of noise in the control parameters is mitigated, improving accuracy. This work was supported in part by NASA, AFRL, ODNI, and IARPA.

Quantum learning of classical stochastic processes: The completely positive realization problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monràs, Alex; Centre for Quantum Technologies, National University of Singapore, 3 Science Drive 2, Singapore 117543; Winter, Andreas

2016-01-15

Among several tasks in Machine Learning, a specially important one is the problem of inferring the latent variables of a system and their causal relations with the observed behavior. A paradigmatic instance of this is the task of inferring the hidden Markov model underlying a given stochastic process. This is known as the positive realization problem (PRP), [L. Benvenuti and L. Farina, IEEE Trans. Autom. Control 49(5), 651–664 (2004)] and constitutes a central problem in machine learning. The PRP and its solutions have far-reaching consequences in many areas of systems and control theory, and is nowadays an important piece inmore » the broad field of positive systems theory. We consider the scenario where the latent variables are quantum (i.e., quantum states of a finite-dimensional system) and the system dynamics is constrained only by physical transformations on the quantum system. The observable dynamics is then described by a quantum instrument, and the task is to determine which quantum instrument — if any — yields the process at hand by iterative application. We take as a starting point the theory of quasi-realizations, whence a description of the dynamics of the process is given in terms of linear maps on state vectors and probabilities are given by linear functionals on the state vectors. This description, despite its remarkable resemblance with the hidden Markov model, or the iterated quantum instrument, is however devoid of any stochastic or quantum mechanical interpretation, as said maps fail to satisfy any positivity conditions. The completely positive realization problem then consists in determining whether an equivalent quantum mechanical description of the same process exists. We generalize some key results of stochastic realization theory, and show that the problem has deep connections with operator systems theory, giving possible insight to the lifting problem in quotient operator systems. Our results have potential applications in quantum machine learning, device-independent characterization and reverse-engineering of stochastic processes and quantum processors, and more generally, of dynamical processes with quantum memory [M. Guţă, Phys. Rev. A 83(6), 062324 (2011); M. Guţă and N. Yamamoto, e-print http://arxiv.org/abs/1303.3771 (2013)].« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santos, Bruno; Carvalho, Paulo F.; Rodrigues, A.P.

The ATCA standard specifies a mandatory Shelf Manager (ShM) unit which is a key element for the system operation. It includes the Intelligent Platform Management Controller (IPMC) which monitors the system health, retrieves inventory information and controls the Field Replaceable Units (FRUs). These elements enable the intelligent health monitoring, providing high-availability and safety operation, ensuring the correct system operation. For critical systems like ones of tokamak ITER these features are mandatory to support the long pulse operation. The Nominal Device Support (NDS) was designed and developed for the ITER CODAC Core System (CCS), which will be the responsible for plantmore » Instrumentation and Control (I and C), supervising and monitoring on ITER. It generalizes the Enhanced Physics and Industrial Control System (EPICS) device support interface for Data Acquisition (DAQ) and timing devices. However the support for health management features and ATCA ShM are not yet provided. This paper presents the implementation and test of a NDS for the ATCA ShM, using the ITER Fast Plant System Controller (FPSC) prototype environment. This prototype is fully compatible with the ITER CCS and uses the EPICS Channel Access (CA) protocol as the interface with the Plant Operation Network (PON). The implemented solution running in an EPICS Input / Output Controller (IOC) provides Process Variables (PV) to the PON network with the system information. These PVs can be used for control and monitoring by all CA clients, such as EPICS user interface clients and alarm systems. The results are presented, demonstrating the fully integration and the usability of this solution. (authors)« less

Control advances for achieving the ITER baseline scenario on KSTAR

NASA Astrophysics Data System (ADS)

Eidietis, N. W.; Barr, J.; Hahn, S. H.; Humphreys, D. A.; in, Y. K.; Jeon, Y. M.; Lanctot, M. J.; Mueller, D.; Walker, M. L.

2017-10-01

Control methodologies developed to enable successful production of ITER baseline scenario (IBS) plasmas on the superconducting KSTAR tokamak are presented: decoupled vertical control (DVC), real-time feedforward (rtFF) calculation, and multi-input multi-output (MIMO) X-point control. DVC provides fast vertical control with the in-vessel control coils (IVCC) while sharing slow vertical control with the poloidal field (PF) coils to avoid IVCC saturation. rtFF compensates for inaccuracies in offline PF current feedforward programming, allowing reduction or removal of integral gain (and its detrimental phase lag) from the shape controller. Finally, MIMO X-point control provides accurate positioning of the X-point despite low controllability due to the large distance between coils and plasma. Combined, these techniques enabled achievement of IBS parameters (q95 = 3.2, βN = 2) with a scaled ITER shape on KSTAR. n =2 RMP response displays a strong dependence upon this shaping. Work supported by the US DOE under Award DE-SC0010685 and the KSTAR project.

PID-based error signal modeling

NASA Astrophysics Data System (ADS)

Yohannes, Tesfay

1997-10-01

This paper introduces a PID based signal error modeling. The error modeling is based on the betterment process. The resulting iterative learning algorithm is introduced and a detailed proof is provided for both linear and nonlinear systems.

Non-axisymmetric ideal equilibrium and stability of ITER plasmas with rotating RMPs

NASA Astrophysics Data System (ADS)

Ham, C. J.; Cramp, R. G. J.; Gibson, S.; Lazerson, S. A.; Chapman, I. T.; Kirk, A.

2016-08-01

The magnetic perturbations produced by the resonant magnetic perturbation (RMP) coils will be rotated in ITER so that the spiral patterns due to strike point splitting which are locked to the RMP also rotate. This is to ensure even power deposition on the divertor plates. VMEC equilibria are calculated for different phases of the RMP rotation. It is demonstrated that the off harmonics rotate in the opposite direction to the main harmonic. This is an important topic for future research to control and optimize ITER appropriately. High confinement mode (H-mode) is favourable for the economics of a potential fusion power plant and its use is planned in ITER. However, the high pressure gradient at the edge of the plasma can trigger periodic eruptions called edge localized modes (ELMs). ELMs have the potential to shorten the life of the divertor in ITER (Loarte et al 2003 Plasma Phys. Control. Fusion 45 1549) and so methods for mitigating or suppressing ELMs in ITER will be important. Non-axisymmetric RMP coils will be installed in ITER for ELM control. Sampling theory is used to show that there will be significant a {{n}\\text{coils}}-{{n}\\text{rmp}} harmonic sideband. There are nine coils toroidally in ITER so {{n}\\text{coils}}=9 . This results in a significant n  =  6 component to the {{n}\\text{rmp}}=3 applied field and a significant n  =  5 component to the {{n}\\text{rmp}}=4 applied field. Although the vacuum field has similar amplitudes of these harmonics the plasma response to the various harmonics dictates the final equilibrium. Magnetic perturbations with toroidal mode number n  =  3 and n  =  4 are applied to a 15 MA, {{q}95}≈ 3 burning ITER plasma. We use a three-dimensional ideal magnetohydrodynamic model (VMEC) to calculate ITER equilibria with applied RMPs and to determine growth rates of infinite n ballooning modes (COBRA). The {{n}\\text{rmp}}=4 case shows little change in ballooning mode growth rate as the RMP is rotated, however there is a change with rotation for the {{n}\\text{rmp}}=3 case.

Data-Driven Property Estimation for Protective Clothing

DTIC Science & Technology

2014-09-01

reliable predictions falls under the rubric “machine learning”. Inspired by the applications of machine learning in pharmaceutical drug design and...using genetic algorithms, for instance— descriptor selection can be automated as well. A well-known structured learning technique—Artificial Neural...descriptors automatically, by iteration, e.g., using a genetic algorithm [49]. 4.2.4 Avoiding Overfitting A peril of all regression—least squares as

Through the Looking Glass: Adult Education through the Lens of the Australian Journal of Adult Learning over Fifty Years

ERIC Educational Resources Information Center

Harris, Roger; Morrison, Anne

2011-01-01

In this paper we review fifty years of articles published in Australian Journal of Adult Learning in its various iterations. We examine the different roles of the journal: to illuminate the history and trends of adult education authors; to be the flagship of the adult education profession in Australia; to reflect on significant national events;…

A Fast Reduced Kernel Extreme Learning Machine.

PubMed

Deng, Wan-Yu; Ong, Yew-Soon; Zheng, Qing-Hua

2016-04-01

In this paper, we present a fast and accurate kernel-based supervised algorithm referred to as the Reduced Kernel Extreme Learning Machine (RKELM). In contrast to the work on Support Vector Machine (SVM) or Least Square SVM (LS-SVM), which identifies the support vectors or weight vectors iteratively, the proposed RKELM randomly selects a subset of the available data samples as support vectors (or mapping samples). By avoiding the iterative steps of SVM, significant cost savings in the training process can be readily attained, especially on Big datasets. RKELM is established based on the rigorous proof of universal learning involving reduced kernel-based SLFN. In particular, we prove that RKELM can approximate any nonlinear functions accurately under the condition of support vectors sufficiency. Experimental results on a wide variety of real world small instance size and large instance size applications in the context of binary classification, multi-class problem and regression are then reported to show that RKELM can perform at competitive level of generalized performance as the SVM/LS-SVM at only a fraction of the computational effort incurred. Copyright © 2015 Elsevier Ltd. All rights reserved.

Prediction of final error level in learning and repetitive control

NASA Astrophysics Data System (ADS)

Levoci, Peter A.

Repetitive control (RC) is a field that creates controllers to eliminate the effects of periodic disturbances on a feedback control system. The methods have applications in spacecraft problems, to isolate fine pointing equipment from periodic vibration disturbances such as slight imbalances in momentum wheels or cryogenic pumps. A closely related field of control design is iterative learning control (ILC) which aims to eliminate tracking error in a task that repeats, each time starting from the same initial condition. Experiments done on a robot at NASA Langley Research Center showed that the final error levels produced by different candidate repetitive and learning controllers can be very different, even when each controller is analytically proven to converge to zero error in the deterministic case. Real world plant and measurement noise and quantization noise (from analog to digital and digital to analog converters) in these control methods are acted on as if they were error sources that will repeat and should be cancelled, which implies that the algorithms amplify such errors. Methods are developed that predict the final error levels of general first order ILC, of higher order ILC including current cycle learning, and of general RC, in the presence of noise, using frequency response methods. The method involves much less computation than the corresponding time domain approach that involves large matrices. The time domain approach was previously developed for ILC and handles a certain class of ILC methods. Here methods are created to include zero-phase filtering that is very important in creating practical designs. Also, time domain methods are developed for higher order ILC and for repetitive control. Since RC and ILC must be implemented digitally, all of these methods predict final error levels at the sample times. It is shown here that RC can easily converge to small error levels between sample times, but that ILC in most applications will have large and diverging intersample error if in fact zero error is reached at the sample times. This is independent of the ILC law used, and is purely a property of the physical system. Methods are developed to address this issue.

Adaptive Management of Ecosystems

EPA Science Inventory

Adaptive management is an approach to natural resource management that emphasizes learning through management. As such, management may be treated as experiment, with replication, or management may be conducted in an iterative manner. Although the concept has resonated with many...

Introductory Statistics in the Garden

ERIC Educational Resources Information Center

Wagaman, John C.

2017-01-01

This article describes four semesters of introductory statistics courses that incorporate service learning and gardening into the curriculum with applications of the binomial distribution, least squares regression and hypothesis testing. The activities span multiple semesters and are iterative in nature.

Ground Truth Creation for Complex Clinical NLP Tasks - an Iterative Vetting Approach and Lessons Learned.

PubMed

Liang, Jennifer J; Tsou, Ching-Huei; Devarakonda, Murthy V

2017-01-01

Natural language processing (NLP) holds the promise of effectively analyzing patient record data to reduce cognitive load on physicians and clinicians in patient care, clinical research, and hospital operations management. A critical need in developing such methods is the "ground truth" dataset needed for training and testing the algorithms. Beyond localizable, relatively simple tasks, ground truth creation is a significant challenge because medical experts, just as physicians in patient care, have to assimilate vast amounts of data in EHR systems. To mitigate potential inaccuracies of the cognitive challenges, we present an iterative vetting approach for creating the ground truth for complex NLP tasks. In this paper, we present the methodology, and report on its use for an automated problem list generation task, its effect on the ground truth quality and system accuracy, and lessons learned from the effort.

Publishing activities improves undergraduate biology education

PubMed Central

Smith, Michelle K

2018-01-01

Abstract To improve undergraduate biology education, there is an urgent need for biology instructors to publish their innovative active-learning instructional materials in peer-reviewed journals. To do this, instructors can measure student knowledge about a variety of biology concepts, iteratively design activities, explore student learning outcomes and publish the results. Creating a set of well-vetted activities, searchable through a journal interface, saves other instructors time and encourages the use of active-learning instructional practices. For authors, these publications offer new opportunities to collaborate and can provide evidence of a commitment to using active-learning instructional techniques in the classroom. PMID:29672697

Publishing activities improves undergraduate biology education.

PubMed

Smith, Michelle K

2018-06-01

To improve undergraduate biology education, there is an urgent need for biology instructors to publish their innovative active-learning instructional materials in peer-reviewed journals. To do this, instructors can measure student knowledge about a variety of biology concepts, iteratively design activities, explore student learning outcomes and publish the results. Creating a set of well-vetted activities, searchable through a journal interface, saves other instructors time and encourages the use of active-learning instructional practices. For authors, these publications offer new opportunities to collaborate and can provide evidence of a commitment to using active-learning instructional techniques in the classroom.

Model-free learning on robot kinematic chains using a nested multi-agent topology

NASA Astrophysics Data System (ADS)

Karigiannis, John N.; Tzafestas, Costas S.

2016-11-01

This paper proposes a model-free learning scheme for the developmental acquisition of robot kinematic control and dexterous manipulation skills. The approach is based on a nested-hierarchical multi-agent architecture that intuitively encapsulates the topology of robot kinematic chains, where the activity of each independent degree-of-freedom (DOF) is finally mapped onto a distinct agent. Each one of those agents progressively evolves a local kinematic control strategy in a game-theoretic sense, that is, based on a partial (local) view of the whole system topology, which is incrementally updated through a recursive communication process according to the nested-hierarchical topology. Learning is thus approached not through demonstration and training but through an autonomous self-exploration process. A fuzzy reinforcement learning scheme is employed within each agent to enable efficient exploration in a continuous state-action domain. This paper constitutes in fact a proof of concept, demonstrating that global dexterous manipulation skills can indeed evolve through such a distributed iterative learning of local agent sensorimotor mappings. The main motivation behind the development of such an incremental multi-agent topology is to enhance system modularity, to facilitate extensibility to more complex problem domains and to improve robustness with respect to structural variations including unpredictable internal failures. These attributes of the proposed system are assessed in this paper through numerical experiments in different robot manipulation task scenarios, involving both single and multi-robot kinematic chains. The generalisation capacity of the learning scheme is experimentally assessed and robustness properties of the multi-agent system are also evaluated with respect to unpredictable variations in the kinematic topology. Furthermore, these numerical experiments demonstrate the scalability properties of the proposed nested-hierarchical architecture, where new agents can be recursively added in the hierarchy to encapsulate individual active DOFs. The results presented in this paper demonstrate the feasibility of such a distributed multi-agent control framework, showing that the solutions which emerge are plausible and near-optimal. Numerical efficiency and computational cost issues are also discussed.

A Fast Optimization Method for General Binary Code Learning.

PubMed

Shen, Fumin; Zhou, Xiang; Yang, Yang; Song, Jingkuan; Shen, Heng; Tao, Dacheng

2016-09-22

Hashing or binary code learning has been recognized to accomplish efficient near neighbor search, and has thus attracted broad interests in recent retrieval, vision and learning studies. One main challenge of learning to hash arises from the involvement of discrete variables in binary code optimization. While the widely-used continuous relaxation may achieve high learning efficiency, the pursued codes are typically less effective due to accumulated quantization error. In this work, we propose a novel binary code optimization method, dubbed Discrete Proximal Linearized Minimization (DPLM), which directly handles the discrete constraints during the learning process. Specifically, the discrete (thus nonsmooth nonconvex) problem is reformulated as minimizing the sum of a smooth loss term with a nonsmooth indicator function. The obtained problem is then efficiently solved by an iterative procedure with each iteration admitting an analytical discrete solution, which is thus shown to converge very fast. In addition, the proposed method supports a large family of empirical loss functions, which is particularly instantiated in this work by both a supervised and an unsupervised hashing losses, together with the bits uncorrelation and balance constraints. In particular, the proposed DPLM with a supervised `2 loss encodes the whole NUS-WIDE database into 64-bit binary codes within 10 seconds on a standard desktop computer. The proposed approach is extensively evaluated on several large-scale datasets and the generated binary codes are shown to achieve very promising results on both retrieval and classification tasks.

Sparsity-constrained PET image reconstruction with learned dictionaries

NASA Astrophysics Data System (ADS)

Tang, Jing; Yang, Bao; Wang, Yanhua; Ying, Leslie

2016-09-01

PET imaging plays an important role in scientific and clinical measurement of biochemical and physiological processes. Model-based PET image reconstruction such as the iterative expectation maximization algorithm seeking the maximum likelihood solution leads to increased noise. The maximum a posteriori (MAP) estimate removes divergence at higher iterations. However, a conventional smoothing prior or a total-variation (TV) prior in a MAP reconstruction algorithm causes over smoothing or blocky artifacts in the reconstructed images. We propose to use dictionary learning (DL) based sparse signal representation in the formation of the prior for MAP PET image reconstruction. The dictionary to sparsify the PET images in the reconstruction process is learned from various training images including the corresponding MR structural image and a self-created hollow sphere. Using simulated and patient brain PET data with corresponding MR images, we study the performance of the DL-MAP algorithm and compare it quantitatively with a conventional MAP algorithm, a TV-MAP algorithm, and a patch-based algorithm. The DL-MAP algorithm achieves improved bias and contrast (or regional mean values) at comparable noise to what the other MAP algorithms acquire. The dictionary learned from the hollow sphere leads to similar results as the dictionary learned from the corresponding MR image. Achieving robust performance in various noise-level simulation and patient studies, the DL-MAP algorithm with a general dictionary demonstrates its potential in quantitative PET imaging.

Developing Humanoid Robots for Real-World Environments

NASA Technical Reports Server (NTRS)

Stoica, Adrian; Kuhlman, Michael; Assad, Chris; Keymeulen, Didier

2008-01-01

Humanoids are steadily improving in appearance and functionality demonstrated in controlled environments. To address the challenges of operation in the real-world, researchers have proposed the use of brain-inspired architectures for robot control, and the use of robot learning techniques that enable the robot to acquire and tune skills and behaviours. In the first part of the paper we introduce new concepts and results in these two areas. First, we present a cerebellum-inspired model that demonstrated efficiency in the sensory-motor control of anthropomorphic arms, and in gait control of dynamic walkers. Then, we present a set of new ideas related to robot learning, emphasizing the importance of developing teaching techniques that support learning. In the second part of the paper we propose the use in robotics of the iterative and incremental development methodologies, in the context of practical task-oriented applications. These methodologies promise to rapidly reach system-level integration, and to early identify system-level weaknesses to focus on. We apply this methodology in a task targeting the automated assembly of a modular structure using HOAP-2. We confirm this approach led to rapid development of a end-to-end capability, and offered guidance on which technologies to focus on for gradual improvement of a complete functional system. It is believed that providing Grand Challenge type milestones in practical task-oriented applications accelerates development. As a meaningful target in short-mid term we propose the 'IKEA Challenge', aimed at the demonstration of autonomous assembly of various pieces of furniture, from the box, following included written/drawn instructions.

Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding.

PubMed

Gao, Wei; Kwong, Sam; Jia, Yuheng

2017-08-25

In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in High Efficiency Video Coding (HEVC). First, a support vector machine (SVM) based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level Rate-Distortion (R-D) model. The legacy "chicken-and-egg" dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model based utility function is proved, and Nash bargaining solution (NBS) is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level Quantization parameter (QP) change. Lastly, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

JRTF: A Flexible Software Framework for Real-Time Control in Magnetic Confinement Nuclear Fusion Experiments

NASA Astrophysics Data System (ADS)

Zhang, M.; Zheng, G. Z.; Zheng, W.; Chen, Z.; Yuan, T.; Yang, C.

2016-04-01

The magnetic confinement nuclear fusion experiments require various real-time control applications like plasma control. ITER has designed the Fast Plant System Controller (FPSC) for this job. ITER provided hardware and software standards and guidelines for building a FPSC. In order to develop various real-time FPSC applications efficiently, a flexible real-time software framework called J-TEXT real-time framework (JRTF) is developed by J-TEXT tokamak team. JRTF allowed developers to implement different functions as independent and reusable modules called Application Blocks (AB). The AB developers only need to focus on implementing the control tasks or the algorithms. The timing, scheduling, data sharing and eventing are handled by the JRTF pipelines. JRTF provides great flexibility on developing ABs. Unit test against ABs can be developed easily and ABs can even be used in non-JRTF applications. JRTF also provides interfaces allowing JRTF applications to be configured and monitored at runtime. JRTF is compatible with ITER standard FPSC hardware and ITER (Control, Data Access and Communication) CODAC Core software. It can be configured and monitored using (Experimental Physics and Industrial Control System) EPICS. Moreover the JRTF can be ported to different platforms and be integrated with supervisory control software other than EPICS. The paper presents the design and implementation of JRTF as well as brief test results.

Is there a need for a specific educational scholarship for using e-learning in medical education?

PubMed

Sandars, John; Goh, Poh Sun

2016-10-01

We propose the need for a specific educational scholarship when using e-learning in medical education. Effective e-learning has additional factors that require specific critical attention, including the design and delivery of e-learning. An important aspect is the recognition that e-learning is a complex intervention, with several interconnecting components that have to be aligned. This alignment requires an essential iterative development process with usability testing. Effectiveness of e-learning in one context may not be fully realized in another context unless there is further consideration of applicability and scalability. We recommend a participatory approach for an educational scholarship for using e-learning in medical education, such as by action research or design-based research.

Udzawa-type iterative method with parareal preconditioner for a parabolic optimal control problem

NASA Astrophysics Data System (ADS)

Lapin, A.; Romanenko, A.

2016-11-01

The article deals with the optimal control problem with the parabolic equation as state problem. There are point-wise constraints on the state and control functions. The objective functional involves the observation given in the domain at each moment. The conditions for convergence Udzawa's type iterative method are given. The parareal method to inverse preconditioner is given. The results of calculations are presented.

Measured vs. Predicted Pedestal Pressure During RMP ELM Control in DIII-D

NASA Astrophysics Data System (ADS)

Zywicki, Bailey; Fenstermacher, Max; Groebner, Richard; Meneghini, Orso

2017-10-01

From database analysis of DIII-D plasmas with Resonant Magnetic Perturbations (RMPs) for ELM control, we will compare the experimental pedestal pressure (p_ped) to EPED code predictions and present the dependence of any p_ped differences from EPED on RMP parameters not included in the EPED model e.g. RMP field strength, toroidal and poloidal spectrum etc. The EPED code, based on Peeling-Ballooning and Kinetic Ballooning instability constraints, will also be used by ITER to predict the H-mode p_ped without RMPs. ITER plans to use RMPs as an effective ELM control method. The need to control ELMs in ITER is of the utmost priority, as it directly correlates to the lifetime of the plasma facing components. An accurate means of determining the impact of RMP ELM control on the p_ped is needed, because the device fusion power is strongly dependent on p_ped. With this new collection of data, we aim to provide guidance to predictions of the ITER pedestal during RMP ELM control that can be incorporated in a future predictive code. Work supported in part by US DoE under the Science Undergraduate Laboratory Internship (SULI) program and under DE-FC02-04ER54698, and DE-AC52-07NA27344.

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

PubMed

Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

2016-06-01

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

Modelling controlled VDE's and ramp-down scenarios in ITER

NASA Astrophysics Data System (ADS)

Lodestro, L. L.; Kolesnikov, R. A.; Meyer, W. H.; Pearlstein, L. D.; Humphreys, D. A.; Walker, M. L.

2011-10-01

Following the design reviews of recent years, the ITER poloidal-field coil-set design, including in-vessel coils (VS3), and the divertor configuration have settled down. The divertor and its material composition (the latter has not been finalized) affect the development of fiducial equilibria and scenarios together with the coils through constraints on strike-point locations and limits on the PF and control systems. Previously we have reported on our studies simulating controlled vertical events in ITER with the JCT 2001 controller to which we added a PID VS3 circuit. In this paper we report and compare controlled VDE results using an optimized integrated VS and shape controller in the updated configuration. We also present our recent simulations of alternate ramp-down scenarios, looking at the effects of ramp-down time and shape strategies, using these controllers. This work performed under the auspices of the U.S. Department of Energy by LLNL under Contract DE-AC52-07NA27344.

Advanced Agent Methods in Adversarial Environment

DTIC Science & Technology

2005-11-30

2 Contents Contents 1 Introduction – Technical Statement of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.1...37 5.4.1 Deriving Trust Observations from Coalition Cooperation Results . . . . . . . . . . . 37 Contents 3 5.4.2 Iterative Learning of...85 4 Contents A.3.5 Class Finder

Learning by Restorying

ERIC Educational Resources Information Center

Slabon, Wayne A.; Richards, Randy L.; Dennen, Vanessa P.

2014-01-01

In this paper, we introduce restorying, a pedagogical approach based on social constructivism that employs successive iterations of rewriting and discussing personal, student-generated, domain-relevant stories to promote conceptual application, critical thinking, and ill-structured problem solving skills. Using a naturalistic, qualitative case…

Automating Rule Strengths in Expert Systems.

DTIC Science & Technology

1987-05-01

systems were designed in an incremental, iterative way. One of the most easily identifiable phases in this process, sometimes called tuning, consists...attenuators. The designer of the knowledge-based system must determine (synthesize) or adjust (xfine, if estimates of the values are given) these...values. We consider two ways in which the designer can learn the values. We call the first model of learning the complete case and the second model the

Learning to read aloud: A neural network approach using sparse distributed memory

NASA Technical Reports Server (NTRS)

Joglekar, Umesh Dwarkanath

1989-01-01

An attempt to solve a problem of text-to-phoneme mapping is described which does not appear amenable to solution by use of standard algorithmic procedures. Experiments based on a model of distributed processing are also described. This model (sparse distributed memory (SDM)) can be used in an iterative supervised learning mode to solve the problem. Additional improvements aimed at obtaining better performance are suggested.

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Experiments on individual strategy updating in iterated snowdrift game under random rematching.

PubMed

Qi, Hang; Ma, Shoufeng; Jia, Ning; Wang, Guangchao

2015-03-07

How do people actually play the iterated snowdrift games, particularly under random rematching protocol is far from well explored. Two sets of laboratory experiments on snowdrift game were conducted to investigate human strategy updating rules. Four groups of subjects were modeled by experience-weighted attraction learning theory at individual-level. Three out of the four groups (75%) passed model validation. Substantial heterogeneity is observed among the players who update their strategies in four typical types, whereas rare people behave like belief-based learners even under fixed pairing. Most subjects (63.9%) adopt the reinforcement learning (or alike) rules; but, interestingly, the performance of averaged reinforcement learners suffered. It is observed that two factors seem to benefit players in competition, i.e., the sensitivity to their recent experiences and the overall consideration of forgone payoffs. Moreover, subjects with changing opponents tend to learn faster based on their own recent experience, and display more diverse strategy updating rules than they do with fixed opponent. These findings suggest that most of subjects do apply reinforcement learning alike updating rules even under random rematching, although these rules may not improve their performance. The findings help evolutionary biology researchers to understand sophisticated human behavioral strategies in social dilemmas. Copyright © 2015 Elsevier Ltd. All rights reserved.

US NDC Modernization Iteration E1 Prototyping Report: Processing Control Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prescott, Ryan; Hamlet, Benjamin R.

2014-12-01

During the first iteration of the US NDC Modernization Elaboration phase (E1), the SNL US NDC modernization project team developed an initial survey of applicable COTS solutions, and established exploratory prototyping related to the processing control framework in support of system architecture definition. This report summarizes these activities and discusses planned follow-on work.

Transforming paper-based assessment forms to a digital format: Exemplified by the Housing Enabler prototype app.

PubMed

Svarre, Tanja; Lunn, Tine Bieber Kirkegaard; Helle, Tina

2017-11-01

The aim of this paper is to provide the reader with an overall impression of the stepwise user-centred design approach including the specific methods used and lessons learned when transforming paper-based assessment forms into a prototype app, taking the Housing Enabler as an example. Four design iterations were performed, building on a domain study, workshops, expert evaluation and controlled and realistic usability tests. The user-centred design process involved purposefully selected participants with different Housing Enabler knowledge and housing adaptation experience. The design iterations resulted in the development of a Housing Enabler prototype app. The prototype app has several features and options that are new compared with the original paper-based Housing Enabler assessment form. These new features include a user friendly overview of the assessment form; easy navigation by swiping back and forth between items; onsite data analysis; and ranking of the accessibility score, photo documentation and a data export facility. Based on the presented stepwise approach, a high-fidelity Housing Enabler prototype app was successfully developed. The development process has emphasized the importance of combining design participants' knowledge and experiences, and has shown that methods should seem relevant to participants to increase their engagement.

Cyclic Game Dynamics Driven by Iterated Reasoning

PubMed Central

Frey, Seth; Goldstone, Robert L.

2013-01-01

Recent theories from complexity science argue that complex dynamics are ubiquitous in social and economic systems. These claims emerge from the analysis of individually simple agents whose collective behavior is surprisingly complicated. However, economists have argued that iterated reasoning–what you think I think you think–will suppress complex dynamics by stabilizing or accelerating convergence to Nash equilibrium. We report stable and efficient periodic behavior in human groups playing the Mod Game, a multi-player game similar to Rock-Paper-Scissors. The game rewards subjects for thinking exactly one step ahead of others in their group. Groups that play this game exhibit cycles that are inconsistent with any fixed-point solution concept. These cycles are driven by a “hopping” behavior that is consistent with other accounts of iterated reasoning: agents are constrained to about two steps of iterated reasoning and learn an additional one-half step with each session. If higher-order reasoning can be complicit in complex emergent dynamics, then cyclic and chaotic patterns may be endogenous features of real-world social and economic systems. PMID:23441191

Reinforcement-learning-based output-feedback control of nonstrict nonlinear discrete-time systems with application to engine emission control.

PubMed

Shih, Peter; Kaul, Brian C; Jagannathan, Sarangapani; Drallmeier, James A

2009-10-01

A novel reinforcement-learning-based output adaptive neural network (NN) controller, which is also referred to as the adaptive-critic NN controller, is developed to deliver the desired tracking performance for a class of nonlinear discrete-time systems expressed in nonstrict feedback form in the presence of bounded and unknown disturbances. The adaptive-critic NN controller consists of an observer, a critic, and two action NNs. The observer estimates the states and output, and the two action NNs provide virtual and actual control inputs to the nonlinear discrete-time system. The critic approximates a certain strategic utility function, and the action NNs minimize the strategic utility function and control inputs. All NN weights adapt online toward minimization of a performance index, utilizing the gradient-descent-based rule, in contrast with iteration-based adaptive-critic schemes. Lyapunov functions are used to show the stability of the closed-loop tracking error, weights, and observer estimates. Separation and certainty equivalence principles, persistency of excitation condition, and linearity in the unknown parameter assumption are not needed. Experimental results on a spark ignition (SI) engine operating lean at an equivalence ratio of 0.75 show a significant (25%) reduction in cyclic dispersion in heat release with control, while the average fuel input changes by less than 1% compared with the uncontrolled case. Consequently, oxides of nitrogen (NO(x)) drop by 30%, and unburned hydrocarbons drop by 16% with control. Overall, NO(x)'s are reduced by over 80% compared with stoichiometric levels.

Intelligent process mapping through systematic improvement of heuristics

NASA Technical Reports Server (NTRS)

Ieumwananonthachai, Arthur; Aizawa, Akiko N.; Schwartz, Steven R.; Wah, Benjamin W.; Yan, Jerry C.

1992-01-01

The present system for automatic learning/evaluation of novel heuristic methods applicable to the mapping of communication-process sets on a computer network has its basis in the testing of a population of competing heuristic methods within a fixed time-constraint. The TEACHER 4.1 prototype learning system implemented or learning new postgame analysis heuristic methods iteratively generates and refines the mappings of a set of communicating processes on a computer network. A systematic exploration of the space of possible heuristic methods is shown to promise significant improvement.

Interdisciplinary Research: Performance and Policy Issues.

ERIC Educational Resources Information Center

Rossini, Frederick A.; Porter, Alan L.

1981-01-01

Successful interdisciplinary research performance, it is suggested, depends on such structural and process factors as leadership, team characteristics, study bounding, iteration, communication patterns, and epistemological factors. Appropriate frameworks for socially organizing the development of knowledge such as common group learning, modeling,…

Electron Cyclotron power management for control of Neoclassical Tearing Modes in the ITER baseline scenario

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poli, Francesca M.; Fredrickson, Eric; Henderson, Mark A.

Time-dependent simulations are used to evolve plasma discharges in combination with a Modified Rutherford equation (MRE) for calculation of Neoclassical Tearing Mode (NTM) stability in response to Electron Cyclotron (EC) feedback control in ITER. The main application of this integrated approach is to support the development of control algorithms by analyzing the plasma response with physics-based models and to assess how uncertainties in the detection of the magnetic island and in the EC alignment affect the ability of the ITER EC system to fulfill its purpose. These simulations indicate that it is critical to detect the island as soon asmore » possible, before its size exceeds the EC deposition width, and that maintaining alignment with the rational surface within half of the EC deposition width is needed for stabilization and suppression of the modes, especially in the case of modes with helicity (2,1). A broadening of the deposition profile, for example due to wave scattering by turbulence fluctuations or not well aligned beams, could even be favorable in the case of the (2,1)-NTM, by relaxing an over-focussing of the EC beam and improving the stabilization at the mode onset. Pre-emptive control reduces the power needed for suppression and stabilization in the ITER baseline discharge to a maximum of 5 MW, which should be reserved and available to the Upper Launcher during the entire flattop phase. By assuming continuous triggering of NTMs, with pre-emptive control ITER would be still able to demonstrate a fusion gain of Q=10.« less

Electron cyclotron power management for control of neoclassical tearing modes in the ITER baseline scenario

NASA Astrophysics Data System (ADS)

Poli, F. M.; Fredrickson, E. D.; Henderson, M. A.; Kim, S.-H.; Bertelli, N.; Poli, E.; Farina, D.; Figini, L.

2018-01-01

Time-dependent simulations are used to evolve plasma discharges in combination with a modified Rutherford equation for calculation of neoclassical tearing mode (NTM) stability in response to electron cyclotron (EC) feedback control in ITER. The main application of this integrated approach is to support the development of control algorithms by analyzing the plasma response with physics-based models and to assess how uncertainties in the detection of the magnetic island and in the EC alignment affect the ability of the ITER EC system to fulfill its purpose. Simulations indicate that it is critical to detect the island as soon as possible, before its size exceeds the EC deposition width, and that maintaining alignment with the rational surface within half of the EC deposition width is needed for stabilization and suppression of the modes, especially in the case of modes with helicity (2, 1) . A broadening of the deposition profile, for example due to wave scattering by turbulence fluctuations or not well aligned beams, could even be favorable in the case of the (2, 1)- NTM, by relaxing an over-focussing of the EC beam and improving the stabilization at the mode onset. Pre-emptive control reduces the power needed for suppression and stabilization in the ITER baseline discharge to a maximum of 5 MW, which should be reserved and available to the upper launcher during the entire flattop phase. Assuming continuous triggering of NTMs, with pre-emptive control ITER would be still able to demonstrate a fusion gain of Q=10 .

Electron Cyclotron power management for control of Neoclassical Tearing Modes in the ITER baseline scenario

DOE PAGES

Poli, Francesca M.; Fredrickson, Eric; Henderson, Mark A.; ...

2017-09-21

Time-dependent simulations are used to evolve plasma discharges in combination with a Modified Rutherford equation (MRE) for calculation of Neoclassical Tearing Mode (NTM) stability in response to Electron Cyclotron (EC) feedback control in ITER. The main application of this integrated approach is to support the development of control algorithms by analyzing the plasma response with physics-based models and to assess how uncertainties in the detection of the magnetic island and in the EC alignment affect the ability of the ITER EC system to fulfill its purpose. These simulations indicate that it is critical to detect the island as soon asmore » possible, before its size exceeds the EC deposition width, and that maintaining alignment with the rational surface within half of the EC deposition width is needed for stabilization and suppression of the modes, especially in the case of modes with helicity (2,1). A broadening of the deposition profile, for example due to wave scattering by turbulence fluctuations or not well aligned beams, could even be favorable in the case of the (2,1)-NTM, by relaxing an over-focussing of the EC beam and improving the stabilization at the mode onset. Pre-emptive control reduces the power needed for suppression and stabilization in the ITER baseline discharge to a maximum of 5 MW, which should be reserved and available to the Upper Launcher during the entire flattop phase. By assuming continuous triggering of NTMs, with pre-emptive control ITER would be still able to demonstrate a fusion gain of Q=10.« less

Baseline Architecture of ITER Control System

NASA Astrophysics Data System (ADS)

Wallander, A.; Di Maio, F.; Journeaux, J.-Y.; Klotz, W.-D.; Makijarvi, P.; Yonekawa, I.

2011-08-01

The control system of ITER consists of thousands of computers processing hundreds of thousands of signals. The control system, being the primary tool for operating the machine, shall integrate, control and coordinate all these computers and signals and allow a limited number of staff to operate the machine from a central location with minimum human intervention. The primary functions of the ITER control system are plant control, supervision and coordination, both during experimental pulses and 24/7 continuous operation. The former can be split in three phases; preparation of the experiment by defining all parameters; executing the experiment including distributed feed-back control and finally collecting, archiving, analyzing and presenting all data produced by the experiment. We define the control system as a set of hardware and software components with well defined characteristics. The architecture addresses the organization of these components and their relationship to each other. We distinguish between physical and functional architecture, where the former defines the physical connections and the latter the data flow between components. In this paper, we identify the ITER control system based on the plant breakdown structure. Then, the control system is partitioned into a workable set of bounded subsystems. This partition considers at the same time the completeness and the integration of the subsystems. The components making up subsystems are identified and defined, a naming convention is introduced and the physical networks defined. Special attention is given to timing and real-time communication for distributed control. Finally we discuss baseline technologies for implementing the proposed architecture based on analysis, market surveys, prototyping and benchmarking carried out during the last year.

Engineering Design of ITER Prototype Fast Plant System Controller

NASA Astrophysics Data System (ADS)

Goncalves, B.; Sousa, J.; Carvalho, B.; Rodrigues, A. P.; Correia, M.; Batista, A.; Vega, J.; Ruiz, M.; Lopez, J. M.; Rojo, R. Castro; Wallander, A.; Utzel, N.; Neto, A.; Alves, D.; Valcarcel, D.

2011-08-01

The ITER control, data access and communication (CODAC) design team identified the need for two types of plant systems. A slow control plant system is based on industrial automation technology with maximum sampling rates below 100 Hz, and a fast control plant system is based on embedded technology with higher sampling rates and more stringent real-time requirements than that required for slow controllers. The latter is applicable to diagnostics and plant systems in closed-control loops whose cycle times are below 1 ms. Fast controllers will be dedicated industrial controllers with the ability to supervise other fast and/or slow controllers, interface to actuators and sensors and, if necessary, high performance networks. Two prototypes of a fast plant system controller specialized for data acquisition and constrained by ITER technological choices are being built using two different form factors. This prototyping activity contributes to the Plant Control Design Handbook effort of standardization, specifically regarding fast controller characteristics. Envisaging a general purpose fast controller design, diagnostic use cases with specific requirements were analyzed and will be presented along with the interface with CODAC and sensors. The requirements and constraints that real-time plasma control imposes on the design were also taken into consideration. Functional specifications and technology neutral architecture, together with its implications on the engineering design, were considered. The detailed engineering design compliant with ITER standards was performed and will be discussed in detail. Emphasis will be given to the integration of the controller in the standard CODAC environment. Requirements for the EPICS IOC providing the interface to the outside world, the prototype decisions on form factor, real-time operating system, and high-performance networks will also be discussed, as well as the requirements for data streaming to CODAC for visualization and archiving.

An implicit-iterative solution of the heat conduction equation with a radiation boundary condition

NASA Technical Reports Server (NTRS)

Williams, S. D.; Curry, D. M.

1977-01-01

For the problem of predicting one-dimensional heat transfer between conducting and radiating mediums by an implicit finite difference method, four different formulations were used to approximate the surface radiation boundary condition while retaining an implicit formulation for the interior temperature nodes. These formulations are an explicit boundary condition, a linearized boundary condition, an iterative boundary condition, and a semi-iterative boundary method. The results of these methods in predicting surface temperature on the space shuttle orbiter thermal protection system model under a variety of heating rates were compared. The iterative technique caused the surface temperature to be bounded at each step. While the linearized and explicit methods were generally more efficient, the iterative and semi-iterative techniques provided a realistic surface temperature response without requiring step size control techniques.

VIMOS Instrument Control Software Design: an Object Oriented Approach

NASA Astrophysics Data System (ADS)

Brau-Nogué, Sylvie; Lucuix, Christian

2002-12-01

The Franco-Italian VIMOS instrument is a VIsible imaging Multi-Object Spectrograph with outstanding multiplex capabilities, allowing to take spectra of more than 800 objects simultaneously, or integral field spectroscopy mode in a 54x54 arcsec area. VIMOS is being installed at the Nasmyth focus of the third Unit Telescope of the European Southern Observatory Very Large Telescope (VLT) at Mount Paranal in Chile. This paper will describe the analysis, the design and the implementation of the VIMOS Instrument Control System, using UML notation. Our Control group followed an Object Oriented software process while keeping in mind the ESO VLT standard control concepts. At ESO VLT a complete software library is available. Rather than applying waterfall lifecycle, ICS project used iterative development, a lifecycle consisting of several iterations. Each iteration consisted in : capture and evaluate the requirements, visual modeling for analysis and design, implementation, test, and deployment. Depending of the project phases, iterations focused more or less on specific activity. The result is an object model (the design model), including use-case realizations. An implementation view and a deployment view complement this product. An extract of VIMOS ICS UML model will be presented and some implementation, integration and test issues will be discussed.

EC power management and NTM control in ITER

NASA Astrophysics Data System (ADS)

Poli, Francesca; Fredrickson, E.; Henderson, M.; Bertelli, N.; Farina, D.; Figini, L.; Nowak, S.; Poli, E.; Sauter, O.

2016-10-01

The suppression of Neoclassical Tearing Modes (NTMs) is an essential requirement for the achievement of the demonstration baseline in ITER. The Electron Cyclotron upper launcher is specifically designed to provide highly localized heating and current drive for NTM stabilization. In order to assess the power management for shared applications, we have performed time-dependent simulations for ITER scenarios covering operation from half to full field. The free-boundary TRANSP simulations evolve the magnetic equilibrium and the pressure profiles in response to the heating and current drive sources and are interfaced with a GRE for the evolution of size and frequency of the magnetic islands. Combined with a feedback control of the EC power and the steering angle, these simulations are used to model the plasma response to NTM control, accounting for the misalignment of the EC deposition with the resonant surfaces, uncertainties in the magnetic equilibrium reconstruction and in the magnetic island detection threshold. Simulations indicate that the threshold for detection of the island should not exceed 2-3cm, that pre-emptive control is a preferable option, and that for safe operation the power needed for NTM control should be reserved, rather than shared with other applications. Work supported by ITER under IO/RFQ/13/9550/JTR and by DOE under DE-AC02-09CH11466.

Introducing 12 year-olds to elementary particles

NASA Astrophysics Data System (ADS)

Wiener, Gerfried J.; Schmeling, Sascha M.; Hopf, Martin

2017-07-01

We present a new learning unit, which introduces 12 year-olds to the subatomic structure of matter. The learning unit was iteratively developed as a design-based research project using the technique of probing acceptance. We give a brief overview of the unit’s final version, discuss its key ideas and main concepts, and conclude by highlighting the main implications of our research, which we consider to be most promising for use in the physics classroom.

A strategy with novel evolutionary features for the iterated prisoner's dilemma.

PubMed

Li, Jiawei; Kendall, Graham

2009-01-01

In recent iterated prisoner's dilemma tournaments, the most successful strategies were those that had identification mechanisms. By playing a predetermined sequence of moves and learning from their opponents' responses, these strategies managed to identify their opponents. We believe that these identification mechanisms may be very useful in evolutionary games. In this paper one such strategy, which we call collective strategy, is analyzed. Collective strategies apply a simple but efficient identification mechanism (that just distinguishes themselves from other strategies), and this mechanism allows them to only cooperate with their group members and defect against any others. In this way, collective strategies are able to maintain a stable population in evolutionary iterated prisoner's dilemma. By means of an invasion barrier, this strategy is compared with other strategies in evolutionary dynamics in order to demonstrate its evolutionary features. We also find that this collective behavior assists the evolution of cooperation in specific evolutionary environments.

Accelerated Path-following Iterative Shrinkage Thresholding Algorithm with Application to Semiparametric Graph Estimation

PubMed Central

Zhao, Tuo; Liu, Han

2016-01-01

We propose an accelerated path-following iterative shrinkage thresholding algorithm (APISTA) for solving high dimensional sparse nonconvex learning problems. The main difference between APISTA and the path-following iterative shrinkage thresholding algorithm (PISTA) is that APISTA exploits an additional coordinate descent subroutine to boost the computational performance. Such a modification, though simple, has profound impact: APISTA not only enjoys the same theoretical guarantee as that of PISTA, i.e., APISTA attains a linear rate of convergence to a unique sparse local optimum with good statistical properties, but also significantly outperforms PISTA in empirical benchmarks. As an application, we apply APISTA to solve a family of nonconvex optimization problems motivated by estimating sparse semiparametric graphical models. APISTA allows us to obtain new statistical recovery results which do not exist in the existing literature. Thorough numerical results are provided to back up our theory. PMID:28133430

The Sawtooth Oscillation Effect on Fast-Ion Energy Spectra in ITER Plasma and Neutral Particle Analyzer Measurements

NASA Astrophysics Data System (ADS)

Zaitsev, F. S.; Gorelenkov, N. N.; Petrov, M. P.; Afanasyev, V. I.; Mironov, M. I.

2018-03-01

ITER plasma with parameters close to those with the inductive scenario is considered. The distribution functions of fast ions of deuterium D and tritium T are calculated while taking into account the elastic nuclear collisions with alpha particles 4He using the code FPP-3D. The D and T energy spectra detected by the neutral-particle analyzer (NPA) are determined. The plasma mixing effect on these spectra during sawtooth oscillations is studied. It is shown that the NPA makes it possible to detect sawtooth plasma oscillations in ITER and determine the percentage composition of the D‒T mixture in it both with the presence of instabilities and without them. A conclusion is drawn on the prospects of using NPA data in automatic controllers of thermonuclear fuel isotopic composition control and plasma oscillation regulation in ITER.

Real-time control of divertor detachment in H-mode with impurity seeding using Langmuir probe feedback in JET-ITER-like wall

NASA Astrophysics Data System (ADS)

Guillemaut, C.; Lennholm, M.; Harrison, J.; Carvalho, I.; Valcarcel, D.; Felton, R.; Griph, S.; Hogben, C.; Lucock, R.; Matthews, G. F.; Perez Von Thun, C.; Pitts, R. A.; Wiesen, S.; contributors, JET

2017-04-01

Burning plasmas with 500 MW of fusion power on ITER will rely on partially detached divertor operation to keep target heat loads at manageable levels. Such divertor regimes will be maintained by a real-time control system using the seeding of radiative impurities like nitrogen (N), neon or argon as actuator and one or more diagnostic signals as sensors. Recently, real-time control of divertor detachment has been successfully achieved in Type I ELMy H-mode JET-ITER-like wall discharges by using saturation current (I sat) measurements from divertor Langmuir probes as feedback signals to control the level of N seeding. The degree of divertor detachment is calculated in real-time by comparing the outer target peak I sat measurements to the peak I sat value at the roll-over in order to control the opening of the N injection valve. Real-time control of detachment has been achieved in both fixed and swept strike point experiments. The system has been progressively improved and can now automatically drive the divertor conditions from attached through high recycling and roll-over down to a user-defined level of detachment. Such a demonstration is a successful proof of principle in the context of future operation on ITER which will be extensively equipped with divertor target probes.

Policy Iteration for $H_\\infty $ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao

2018-02-01

Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest -gain and the associated optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.

Control x-ray deformable mirrors with few measurements

NASA Astrophysics Data System (ADS)

Huang, Lei; Xue, Junpeng; Idir, Mourad

2016-09-01

After years of development from a concept to early experimental stage, X-ray Deformable Mirrors (XDMs) are used in many synchrotron/free-electron laser facilities as a standard x-ray optics tool. XDM is becoming an integral part of the present and future large x-ray and EUV projects and will be essential in exploiting the full potential of the new sources currently under construction. The main objective of using XDMs is to correct wavefront errors or to enable variable focus beam sizes at the sample. Due to the coupling among the N actuators of a DM, it is usually necessary to perform a calibration or training process to drive the DM into the target shape. Commonly, in order to optimize the actuators settings to minimize slope/height errors, an initial measurement need to be collected, with all actuators set to 0, and then either N or 2N measurements are necessary learn each actuator behavior sequentially. In total, it means that N+1 or 2N+1 scans are required to perform this learning process. When the actuators number N is important and the actuator response or the necessary metrology is slow then this learning process can be time consuming. In this work, we present a fast and accurate method to drive an x-ray active bimorph mirror to a target shape with only 3 or 4 measurements. Instead of sequentially measuring and calculating the influence functions of all actuators and then predicting the voltages needed for any desired shape, the metrology data are directly used to "guide" the mirror from its current status towards the particular target slope/height via iterative compensations. The feedback for the iteration process is the discrepancy in curvature calculated by using B-spline fitting of the measured height/slope data. In this paper, the feasibility of this simple and effective approach is demonstrated with experiments.

Using a web-based, iterative education model to enhance clinical clerkships.

PubMed

Alexander, Erik K; Bloom, Nurit; Falchuk, Kenneth H; Parker, Michael

2006-10-01

Although most clinical clerkship curricula are designed to provide all students consistent exposure to defined course objectives, it is clear that individual students are diverse in their backgrounds and baseline knowledge. Ideally, the learning process should be individualized towards the strengths and weakness of each student, but, until recently, this has proved prohibitively time-consuming. The authors describe a program to develop and evaluate an iterative, Web-based educational model assessing medical students' knowledge deficits and allowing targeted teaching shortly after their identification. Beginning in 2002, a new educational model was created, validated, and applied in a prospective fashion to medical students during an internal medicine clerkship at Harvard Medical School. Using a Web-based platform, five validated questions were delivered weekly and a specific knowledge deficiency identified. Teaching targeted to the deficiency was provided to an intervention cohort of five to seven students in each clerkship, though not to controls (the remaining 7-10 students). Effectiveness of this model was assessed by performance on the following week's posttest question. Specific deficiencies were readily identified weekly using this model. Throughout the year, however, deficiencies varied unpredictably. Teaching targeted to deficiencies resulted in significantly better performance on follow-up questioning compared to the performance of those who did not receive this intervention. This model was easily applied in an additive fashion to the current curriculum, and student acceptance was high. The authors conclude that a Web-based, iterative assessment model can effectively target specific curricular needs unique to each group; focus teaching in a rapid, formative, and highly efficient manner; and may improve the efficiency of traditional clerkship teaching.

Adaptive Management for Urban Watersheds: The Slavic Village Pilot Project

EPA Science Inventory

Adaptive management is an environmental management strategy that uses an iterative process of decision-making to reduce the uncertainty in environmental management via system monitoring. A central tenet of adaptive management is that management involves a learning process that ca...

Validation of the thermal transport model used for ITER startup scenario predictions with DIII-D experimental data

DOE PAGES

Casper, T. A.; Meyer, W. H.; Jackson, G. L.; ...

2010-12-08

We are exploring characteristics of ITER startup scenarios in similarity experiments conducted on the DIII-D Tokamak. In these experiments, we have validated scenarios for the ITER current ramp up to full current and developed methods to control the plasma parameters to achieve stability. Predictive simulations of ITER startup using 2D free-boundary equilibrium and 1D transport codes rely on accurate estimates of the electron and ion temperature profiles that determine the electrical conductivity and pressure profiles during the current rise. Here we present results of validation studies that apply the transport model used by the ITER team to DIII-D discharge evolutionmore » and comparisons with data from our similarity experiments.« less

Learning and exploration in action-perception loops.

PubMed

Little, Daniel Y; Sommer, Friedrich T

2013-01-01

Discovering the structure underlying observed data is a recurring problem in machine learning with important applications in neuroscience. It is also a primary function of the brain. When data can be actively collected in the context of a closed action-perception loop, behavior becomes a critical determinant of learning efficiency. Psychologists studying exploration and curiosity in humans and animals have long argued that learning itself is a primary motivator of behavior. However, the theoretical basis of learning-driven behavior is not well understood. Previous computational studies of behavior have largely focused on the control problem of maximizing acquisition of rewards and have treated learning the structure of data as a secondary objective. Here, we study exploration in the absence of external reward feedback. Instead, we take the quality of an agent's learned internal model to be the primary objective. In a simple probabilistic framework, we derive a Bayesian estimate for the amount of information about the environment an agent can expect to receive by taking an action, a measure we term the predicted information gain (PIG). We develop exploration strategies that approximately maximize PIG. One strategy based on value-iteration consistently learns faster than previously developed reward-free exploration strategies across a diverse range of environments. Psychologists believe the evolutionary advantage of learning-driven exploration lies in the generalized utility of an accurate internal model. Consistent with this hypothesis, we demonstrate that agents which learn more efficiently during exploration are later better able to accomplish a range of goal-directed tasks. We will conclude by discussing how our work elucidates the explorative behaviors of animals and humans, its relationship to other computational models of behavior, and its potential application to experimental design, such as in closed-loop neurophysiology studies.

Overview of the preliminary design of the ITER plasma control system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Snipes, J. A.; Albanese, R.; Ambrosino, G.

An overview of the Preliminary Design of the ITER Plasma Control System (PCS) is described here, which focusses on the needs for 1st plasma and early plasma operation in hydrogen/helium (H/He) up to a plasma current of 15 MA with moderate auxiliary heating power in low confinement mode (L-mode). Candidate control schemes for basic magnetic control, including divertor operation and kinetic control of the electron density with gas puffing and pellet injection, were developed. Commissioning of the auxiliary heating systems is included as well as support functions for stray field topology and real-time plasma boundary reconstruction. Initial exception handling schemesmore » for faults of essential plant systems and for disruption protection were developed. The PCS architecture was also developed to be capable of handling basic control for early commissioning and the advanced control functions that will be needed for future high performance operation. A plasma control simulator is also being developed to test and validate control schemes. To handle the complexity of the ITER PCS, a systems engineering approach has been adopted with the development of a plasma control database to keep track of all control requirements.« less

Overview of the preliminary design of the ITER plasma control system

NASA Astrophysics Data System (ADS)

Snipes, J. A.; Albanese, R.; Ambrosino, G.; Ambrosino, R.; Amoskov, V.; Blanken, T. C.; Bremond, S.; Cinque, M.; de Tommasi, G.; de Vries, P. C.; Eidietis, N.; Felici, F.; Felton, R.; Ferron, J.; Formisano, A.; Gribov, Y.; Hosokawa, M.; Hyatt, A.; Humphreys, D.; Jackson, G.; Kavin, A.; Khayrutdinov, R.; Kim, D.; Kim, S. H.; Konovalov, S.; Lamzin, E.; Lehnen, M.; Lukash, V.; Lomas, P.; Mattei, M.; Mineev, A.; Moreau, P.; Neu, G.; Nouailletas, R.; Pautasso, G.; Pironti, A.; Rapson, C.; Raupp, G.; Ravensbergen, T.; Rimini, F.; Schneider, M.; Travere, J.-M.; Treutterer, W.; Villone, F.; Walker, M.; Welander, A.; Winter, A.; Zabeo, L.

2017-12-01

An overview of the preliminary design of the ITER plasma control system (PCS) is described here, which focusses on the needs for 1st plasma and early plasma operation in hydrogen/helium (H/He) up to a plasma current of 15 MA with moderate auxiliary heating power in low confinement mode (L-mode). Candidate control schemes for basic magnetic control, including divertor operation and kinetic control of the electron density with gas puffing and pellet injection, were developed. Commissioning of the auxiliary heating systems is included as well as support functions for stray field topology and real-time plasma boundary reconstruction. Initial exception handling schemes for faults of essential plant systems and for disruption protection were developed. The PCS architecture was also developed to be capable of handling basic control for early commissioning and the advanced control functions that will be needed for future high performance operation. A plasma control simulator is also being developed to test and validate control schemes. To handle the complexity of the ITER PCS, a systems engineering approach has been adopted with the development of a plasma control database to keep track of all control requirements.

Overview of the preliminary design of the ITER plasma control system

DOE PAGES

Snipes, J. A.; Albanese, R.; Ambrosino, G.; ...

2017-09-11

An overview of the Preliminary Design of the ITER Plasma Control System (PCS) is described here, which focusses on the needs for 1st plasma and early plasma operation in hydrogen/helium (H/He) up to a plasma current of 15 MA with moderate auxiliary heating power in low confinement mode (L-mode). Candidate control schemes for basic magnetic control, including divertor operation and kinetic control of the electron density with gas puffing and pellet injection, were developed. Commissioning of the auxiliary heating systems is included as well as support functions for stray field topology and real-time plasma boundary reconstruction. Initial exception handling schemesmore » for faults of essential plant systems and for disruption protection were developed. The PCS architecture was also developed to be capable of handling basic control for early commissioning and the advanced control functions that will be needed for future high performance operation. A plasma control simulator is also being developed to test and validate control schemes. To handle the complexity of the ITER PCS, a systems engineering approach has been adopted with the development of a plasma control database to keep track of all control requirements.« less

Off-site training of laparoscopic skills, a scoping review using a thematic analysis.

PubMed

Thinggaard, Ebbe; Kleif, Jakob; Bjerrum, Flemming; Strandbygaard, Jeanett; Gögenur, Ismail; Matthew Ritter, E; Konge, Lars

2016-11-01

The focus of research in simulation-based laparoscopic training has changed from examining whether simulation training works to examining how best to implement it. In laparoscopic skills training, portable and affordable box trainers allow for off-site training. Training outside simulation centers and hospitals can increase access to training, but also poses new challenges to implementation. This review aims to guide implementation of off-site training of laparoscopic skills by critically reviewing the existing literature. An iterative systematic search was carried out in MEDLINE, EMBASE, ERIC, Scopus, and PsychINFO, following a scoping review methodology. The included literature was analyzed iteratively using a thematic analysis approach. The study was reported in accordance with the STructured apprOach to the Reporting In healthcare education of Evidence Synthesis statement. From the search, 22 records were identified and included for analysis. A thematic analysis revealed the themes: access to training, protected training time, distribution of training, goal setting and testing, task design, and unsupervised training. The identified themes were based on learning theories including proficiency-based learning, deliberate practice, and self-regulated learning. Methods of instructional design vary widely in off-site training of laparoscopic skills. Implementation can be facilitated by organizing courses and training curricula following sound education theories such as proficiency-based learning and deliberate practice. Directed self-regulated learning has the potential to improve off-site laparoscopic skills training; however, further studies are needed to demonstrate the effect of this type of instructional design.

PredicT-ML: a tool for automating machine learning model building with big clinical data.

PubMed

Luo, Gang

2016-01-01

Predictive modeling is fundamental to transforming large clinical data sets, or "big clinical data," into actionable knowledge for various healthcare applications. Machine learning is a major predictive modeling approach, but two barriers make its use in healthcare challenging. First, a machine learning tool user must choose an algorithm and assign one or more model parameters called hyper-parameters before model training. The algorithm and hyper-parameter values used typically impact model accuracy by over 40 %, but their selection requires many labor-intensive manual iterations that can be difficult even for computer scientists. Second, many clinical attributes are repeatedly recorded over time, requiring temporal aggregation before predictive modeling can be performed. Many labor-intensive manual iterations are required to identify a good pair of aggregation period and operator for each clinical attribute. Both barriers result in time and human resource bottlenecks, and preclude healthcare administrators and researchers from asking a series of what-if questions when probing opportunities to use predictive models to improve outcomes and reduce costs. This paper describes our design of and vision for PredicT-ML (prediction tool using machine learning), a software system that aims to overcome these barriers and automate machine learning model building with big clinical data. The paper presents the detailed design of PredicT-ML. PredicT-ML will open the use of big clinical data to thousands of healthcare administrators and researchers and increase the ability to advance clinical research and improve healthcare.

Controlled iterative cross-coupling: on the way to the automation of organic synthesis.

PubMed

Wang, Congyang; Glorius, Frank

2009-01-01

Repetition does not hurt! New strategies for the modulation of the reactivity of difunctional building blocks are discussed, allowing the palladium-catalyzed controlled iterative cross-coupling and, thus, the efficient formation of complex molecules of defined size and structure (see scheme). As in peptide synthesis, this development will enable the automation of these reactions. M(PG)=protected metal, M(act)=metal.

Health Worker Focused Distributed Simulation for Improving Capability of Health Systems in Liberia.

PubMed

Gale, Thomas C E; Chatterjee, Arunangsu; Mellor, Nicholas E; Allan, Richard J

2016-04-01

The main goal of this study was to produce an adaptable learning platform using virtual learning and distributed simulation, which can be used to train health care workers, across a wide geographical area, key safety messages regarding infection prevention control (IPC). A situationally responsive agile methodology, Scrum, was used to develop a distributed simulation module using short 1-week iterations and continuous synchronous plus asynchronous communication including end users and IPC experts. The module contained content related to standard IPC precautions (including handwashing techniques) and was structured into 3 distinct sections related to donning, doffing, and hazard perception training. Using Scrum methodology, we were able to link concepts applied to best practices in simulation-based medical education (deliberate practice, continuous feedback, self-assessment, and exposure to uncommon events), pedagogic principles related to adult learning (clear goals, contextual awareness, motivational features), and key learning outcomes regarding IPC, as a rapid response initiative to the Ebola outbreak in West Africa. Gamification approach has been used to map learning mechanics to enhance user engagement. The developed IPC module demonstrates how high-frequency, low-fidelity simulations can be rapidly designed using scrum-based agile methodology. Analytics incorporated into the tool can help demonstrate improved confidence and competence of health care workers who are treating patients within an Ebola virus disease outbreak region. These concepts could be used in a range of evolving disasters where rapid development and communication of key learning messages are required.

Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models

NASA Astrophysics Data System (ADS)

Benedetti, Marcello; Realpe-Gómez, John; Biswas, Rupak; Perdomo-Ortiz, Alejandro

2017-10-01

Mainstream machine-learning techniques such as deep learning and probabilistic programming rely heavily on sampling from generally intractable probability distributions. There is increasing interest in the potential advantages of using quantum computing technologies as sampling engines to speed up these tasks or to make them more effective. However, some pressing challenges in state-of-the-art quantum annealers have to be overcome before we can assess their actual performance. The sparse connectivity, resulting from the local interaction between quantum bits in physical hardware implementations, is considered the most severe limitation to the quality of constructing powerful generative unsupervised machine-learning models. Here, we use embedding techniques to add redundancy to data sets, allowing us to increase the modeling capacity of quantum annealers. We illustrate our findings by training hardware-embedded graphical models on a binarized data set of handwritten digits and two synthetic data sets in experiments with up to 940 quantum bits. Our model can be trained in quantum hardware without full knowledge of the effective parameters specifying the corresponding quantum Gibbs-like distribution; therefore, this approach avoids the need to infer the effective temperature at each iteration, speeding up learning; it also mitigates the effect of noise in the control parameters, making it robust to deviations from the reference Gibbs distribution. Our approach demonstrates the feasibility of using quantum annealers for implementing generative models, and it provides a suitable framework for benchmarking these quantum technologies on machine-learning-related tasks.

Approximated affine projection algorithm for feedback cancellation in hearing aids.

PubMed

Lee, Sangmin; Kim, In-Young; Park, Young-Cheol

2007-09-01

We propose an approximated affine projection (AP) algorithm for feedback cancellation in hearing aids. It is based on the conventional approach using the Gauss-Seidel (GS) iteration, but provides more stable convergence behaviour even with small step sizes. In the proposed algorithm, a residue of the weighted error vector, instead of the current error sample, is used to provide stable convergence. A new learning rate control scheme is also applied to the proposed algorithm to prevent signal cancellation and system instability. The new scheme determines step size in proportion to the prediction factor of the input, so that adaptation is inhibited whenever tone-like signals are present in the input. Simulation results verified the efficiency of the proposed algorithm.

CORSICA modelling of ITER hybrid operation scenarios

NASA Astrophysics Data System (ADS)

Kim, S. H.; Bulmer, R. H.; Campbell, D. J.; Casper, T. A.; LoDestro, L. L.; Meyer, W. H.; Pearlstein, L. D.; Snipes, J. A.

2016-12-01

The hybrid operating mode observed in several tokamaks is characterized by further enhancement over the high plasma confinement (H-mode) associated with reduced magneto-hydro-dynamic (MHD) instabilities linked to a stationary flat safety factor (q ) profile in the core region. The proposed ITER hybrid operation is currently aiming at operating for a long burn duration (>1000 s) with a moderate fusion power multiplication factor, Q , of at least 5. This paper presents candidate ITER hybrid operation scenarios developed using a free-boundary transport modelling code, CORSICA, taking all relevant physics and engineering constraints into account. The ITER hybrid operation scenarios have been developed by tailoring the 15 MA baseline ITER inductive H-mode scenario. Accessible operation conditions for ITER hybrid operation and achievable range of plasma parameters have been investigated considering uncertainties on the plasma confinement and transport. ITER operation capability for avoiding the poloidal field coil current, field and force limits has been examined by applying different current ramp rates, flat-top plasma currents and densities, and pre-magnetization of the poloidal field coils. Various combinations of heating and current drive (H&CD) schemes have been applied to study several physics issues, such as the plasma current density profile tailoring, enhancement of the plasma energy confinement and fusion power generation. A parameterized edge pedestal model based on EPED1 added to the CORSICA code has been applied to hybrid operation scenarios. Finally, fully self-consistent free-boundary transport simulations have been performed to provide information on the poloidal field coil voltage demands and to study the controllability with the ITER controllers. Extended from Proc. 24th Int. Conf. on Fusion Energy (San Diego, 2012) IT/P1-13.

Embodied Design: Constructing Means for Constructing Meaning

ERIC Educational Resources Information Center

Abrahamson, Dor

2009-01-01

Design-based research studies are conducted as iterative implementation-analysis-modification cycles, in which emerging theoretical models and pedagogically plausible activities are reciprocally tuned toward each other as a means of investigating conjectures pertaining to mechanisms underlying content teaching and learning. Yet this approach, even…

Learning Objects: A User-Centered Design Process

ERIC Educational Resources Information Center

Branon, Rovy F., III

2011-01-01

Design research systematically creates or improves processes, products, and programs through an iterative progression connecting practice and theory (Reinking, 2008; van den Akker, 2006). Developing a new instructional systems design (ISD) processes through design research is necessary when new technologies emerge that challenge existing practices…

Designing Needs Statements in a Systematic Iterative Way

ERIC Educational Resources Information Center

Verstegen, D. M. L.; Barnard, Y. F.; Pilot, A.

2009-01-01

Designing specifications for technically advanced instructional products, such as e-learning, simulations or simulators requires different kinds of expertise. The SLIM method proposes to involve all stakeholders from the beginning in a series of workshops under the guidance of experienced instructional designers. These instructional designers…

Enriching regulatory networks by bootstrap learning using optimised GO-based gene similarity and gene links mined from PubMed abstracts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Taylor, Ronald C.; Sanfilippo, Antonio P.; McDermott, Jason E.

2011-02-18

Transcriptional regulatory networks are being determined using “reverse engineering” methods that infer connections based on correlations in gene state. Corroboration of such networks through independent means such as evidence from the biomedical literature is desirable. Here, we explore a novel approach, a bootstrapping version of our previous Cross-Ontological Analytic method (XOA) that can be used for semi-automated annotation and verification of inferred regulatory connections, as well as for discovery of additional functional relationships between the genes. First, we use our annotation and network expansion method on a biological network learned entirely from the literature. We show how new relevant linksmore » between genes can be iteratively derived using a gene similarity measure based on the Gene Ontology that is optimized on the input network at each iteration. Second, we apply our method to annotation, verification, and expansion of a set of regulatory connections found by the Context Likelihood of Relatedness algorithm.« less

The interactive evolution of human communication systems.

PubMed

Fay, Nicolas; Garrod, Simon; Roberts, Leo; Swoboda, Nik

2010-04-01

This paper compares two explanations of the process by which human communication systems evolve: iterated learning and social collaboration. It then reports an experiment testing the social collaboration account. Participants engaged in a graphical communication task either as a member of a community, where they interacted with seven different partners drawn from the same pool, or as a member of an isolated pair, where they interacted with the same partner across the same number of games. Participants' horizontal, pair-wise interactions led "bottom up" to the creation of an effective and efficient shared sign system in the community condition. Furthermore, the community-evolved sign systems were as effective and efficient as the local sign systems developed by isolated pairs. Finally, and as predicted by a social collaboration account, and not by an iterated learning account, interaction was critical to the creation of shared sign systems, with different isolated pairs establishing different local sign systems and different communities establishing different global sign systems. Copyright © 2010 Cognitive Science Society, Inc.

Neural Network Training by Integration of Adjoint Systems of Equations Forward in Time

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Barhen, Jacob (Inventor)

1999-01-01

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically. it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved. but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. Tbc trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

Neural network training by integration of adjoint systems of equations forward in time

NASA Technical Reports Server (NTRS)

Toomarian, Nikzad (Inventor); Barhen, Jacob (Inventor)

1992-01-01

A method and apparatus for supervised neural learning of time dependent trajectories exploits the concepts of adjoint operators to enable computation of the gradient of an objective functional with respect to the various parameters of the network architecture in a highly efficient manner. Specifically, it combines the advantage of dramatic reductions in computational complexity inherent in adjoint methods with the ability to solve two adjoint systems of equations together forward in time. Not only is a large amount of computation and storage saved, but the handling of real-time applications becomes also possible. The invention has been applied it to two examples of representative complexity which have recently been analyzed in the open literature and demonstrated that a circular trajectory can be learned in approximately 200 iterations compared to the 12000 reported in the literature. A figure eight trajectory was achieved in under 500 iterations compared to 20000 previously required. The trajectories computed using our new method are much closer to the target trajectories than was reported in previous studies.

Distant Supervision with Transductive Learning for Adverse Drug Reaction Identification from Electronic Medical Records

PubMed Central

Ikeda, Mitsuru

2017-01-01

Information extraction and knowledge discovery regarding adverse drug reaction (ADR) from large-scale clinical texts are very useful and needy processes. Two major difficulties of this task are the lack of domain experts for labeling examples and intractable processing of unstructured clinical texts. Even though most previous works have been conducted on these issues by applying semisupervised learning for the former and a word-based approach for the latter, they face with complexity in an acquisition of initial labeled data and ignorance of structured sequence of natural language. In this study, we propose automatic data labeling by distant supervision where knowledge bases are exploited to assign an entity-level relation label for each drug-event pair in texts, and then, we use patterns for characterizing ADR relation. The multiple-instance learning with expectation-maximization method is employed to estimate model parameters. The method applies transductive learning to iteratively reassign a probability of unknown drug-event pair at the training time. By investigating experiments with 50,998 discharge summaries, we evaluate our method by varying large number of parameters, that is, pattern types, pattern-weighting models, and initial and iterative weightings of relations for unlabeled data. Based on evaluations, our proposed method outperforms the word-based feature for NB-EM (iEM), MILR, and TSVM with F1 score of 11.3%, 9.3%, and 6.5% improvement, respectively. PMID:29090077

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

NASA Astrophysics Data System (ADS)

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics.

PubMed

Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Learning multimodal dictionaries.

PubMed

Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

2007-09-01

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

How to Compute Labile Metal-Ligand Equilibria

ERIC Educational Resources Information Center

de Levie, Robert

2007-01-01

The different methods used for computing labile metal-ligand complexes, which are suitable for an iterative computer solution, are illustrated. The ligand function has allowed students to relegate otherwise tedious iterations to a computer, while retaining complete control over what is calculated.

Culture: copying, compression, and conventionality.

PubMed

Tamariz, Mónica; Kirby, Simon

2015-01-01

Through cultural transmission, repeated learning by new individuals transforms cultural information, which tends to become increasingly compressible (Kirby, Cornish, & Smith, ; Smith, Tamariz, & Kirby, ). Existing diffusion chain studies include in their design two processes that could be responsible for this tendency: learning (storing patterns in memory) and reproducing (producing the patterns again). This paper manipulates the presence of learning in a simple iterated drawing design experiment. We find that learning seems to be the causal factor behind the increase in compressibility observed in the transmitted information, while reproducing is a source of random heritable innovations. Only a theory invoking these two aspects of cultural learning will be able to explain human culture's fundamental balance between stability and innovation. Copyright © 2014 Cognitive Science Society, Inc.

Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach.

PubMed

Mete, Mutlu; Sakoglu, Unal; Spence, Jeffrey S; Devous, Michael D; Harris, Thomas S; Adinoff, Bryon

2016-10-06

Neuroimaging studies have yielded significant advances in the understanding of neural processes relevant to the development and persistence of addiction. However, these advances have not explored extensively for diagnostic accuracy in human subjects. The aim of this study was to develop a statistical approach, using a machine learning framework, to correctly classify brain images of cocaine-dependent participants and healthy controls. In this study, a framework suitable for educing potential brain regions that differed between the two groups was developed and implemented. Single Photon Emission Computerized Tomography (SPECT) images obtained during rest or a saline infusion in three cohorts of 2-4 week abstinent cocaine-dependent participants (n = 93) and healthy controls (n = 69) were used to develop a classification model. An information theoretic-based feature selection algorithm was first conducted to reduce the number of voxels. A density-based clustering algorithm was then used to form spatially connected voxel clouds in three-dimensional space. A statistical classifier, Support Vectors Machine (SVM), was then used for participant classification. Statistically insignificant voxels of spatially connected brain regions were removed iteratively and classification accuracy was reported through the iterations. The voxel-based analysis identified 1,500 spatially connected voxels in 30 distinct clusters after a grid search in SVM parameters. Participants were successfully classified with 0.88 and 0.89 F-measure accuracies in 10-fold cross validation (10xCV) and leave-one-out (LOO) approaches, respectively. Sensitivity and specificity were 0.90 and 0.89 for LOO; 0.83 and 0.83 for 10xCV. Many of the 30 selected clusters are highly relevant to the addictive process, including regions relevant to cognitive control, default mode network related self-referential thought, behavioral inhibition, and contextual memories. Relative hyperactivity and hypoactivity of regional cerebral blood flow in brain regions in cocaine-dependent participants are presented with corresponding level of significance. The SVM-based approach successfully classified cocaine-dependent and healthy control participants using voxels selected with information theoretic-based and statistical methods from participants' SPECT data. The regions found in this study align with brain regions reported in the literature. These findings support the future use of brain imaging and SVM-based classifier in the diagnosis of substance use disorders and furthering an understanding of their underlying pathology.

Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

NASA Technical Reports Server (NTRS)

He, Yuning; Lee, Herbert K. H.; Davies, Misty D.

2012-01-01

Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.

The presence of a perseverative iterative style in poor vs. good sleepers.

PubMed

Barclay, N L; Gregory, A M

2010-03-01

Catastrophizing is present in worriers and poor sleepers. This study investigates whether poor sleepers possess a 'perseverative iterative style' which predisposes them to catastrophize any topic, regardless of content or affective valence, a style previously found to occur more commonly in worriers as compared to others. Poor (n=23) and good sleepers (n=37) were distinguished using the Pittsburgh Sleep Quality Index (PSQI), from a sample of adults in the general population. Participants were required to catastrophize 2 topics: worries about sleep, and a current personal worry; and to iterate the positive aspects of a hypothetical topic. Poor sleepers catastrophized/iterated more steps to a greater extent than good sleepers to these three interviews, (F(1, 58)=7.35, p<.05). However, after controlling for anxiety and worry, this effect was reduced to non-significance for the 'sleep' and 'worry' topics, suggesting that anxiety may mediate some of the association between catastrophizing and sleep. However there was still a tendency for poor sleepers to iterate more steps to the 'hypothetical' topic, after controlling for anxiety and worry, which also suggests that poor sleepers possess a cognitive style which may predispose them to continue iterating consecutive steps to open-ended tasks regardless of anxiety and worry. Future research should examine whether the presence of this cognitive style is significant in leading to or maintaining insomnia.

Eliciting design patterns for e-learning systems

NASA Astrophysics Data System (ADS)

Retalis, Symeon; Georgiakakis, Petros; Dimitriadis, Yannis

2006-06-01

Design pattern creation, especially in the e-learning domain, is a highly complex process that has not been sufficiently studied and formalized. In this paper, we propose a systematic pattern development cycle, whose most important aspects focus on reverse engineering of existing systems in order to elicit features that are cross-validated through the use of appropriate, authentic scenarios. However, an iterative pattern process is proposed that takes advantage of multiple data sources, thus emphasizing a holistic view of the teaching learning processes. The proposed schema of pattern mining has been extensively validated for Asynchronous Network Supported Collaborative Learning (ANSCL) systems, as well as for other types of tools in a variety of scenarios, with promising results.

Learner Centred Design for a Hybrid Interaction Application

ERIC Educational Resources Information Center

Wood, Simon; Romero, Pablo

2010-01-01

Learner centred design methods highlight the importance of involving the stakeholders of the learning process (learners, teachers, educational researchers) at all stages of the design of educational applications and of refining the design through an iterative prototyping process. These methods have been used successfully when designing systems…

Action Research: Enhancing Classroom Practice and Fulfilling Educational Responsibilities

ERIC Educational Resources Information Center

Young, Mark R.; Rapp, Eve; Murphy, James W.

2010-01-01

Action Research is an applied scholarly paradigm resulting in action for continuous improvement in our teaching and learning techniques offering faculty immediate classroom payback and providing documentation of meeting our educational responsibilities as required by AACSB standards. This article reviews the iterative action research process of…

Design-Based Implementation Research

ERIC Educational Resources Information Center

LeMahieu, Paul G.; Nordstrum, Lee E.; Potvin, Ashley Seidel

2017-01-01

Purpose: This paper is second of seven in this volume elaborating different approaches to quality improvement in education. It delineates a methodology called design-based implementation research (DBIR). The approach used in this paper is aimed at iteratively improving the quality of classroom teaching and learning practices in defined problem…

The Reflective Teacher Leader: An Action Research Model

ERIC Educational Resources Information Center

Furtado, Leena; Anderson, Dawnette

2012-01-01

This study presents four teacher reflections from action research projects ranging from kindergarten to adult school improvements. A teacher leadership matrix guided participants to connect teaching and learning theory to best practices by exploring uncharted territory within an iterative cycle of research and action. Teachers developed the…

Leveraging Failure in Design Research

ERIC Educational Resources Information Center

Lobato, Joanne; Walters, C. David; Hohensee, Charles; Gruver, John; Diamond, Jaime Marie

2015-01-01

Even in the resource-rich, more ideal conditions of many design-based classroom interventions, unexpected events can lead to disappointing results in student learning. However, if later iterations in a design research study are more successful, the previous failures can provide opportunities for comparisons to reveal subtle differences in…

EC power management in ITER for NTM control: the path from the commissioning phase to demonstration discharges

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poli, Francesca M.; Fredrickson, Eric; Henderson, Mark A.

Time dependent simulations that evolve consistently the magnetic equilibrium and plasma pressure profiles and the width and frequency rotation of magnetic islands under the effect of the Electron Cyclotron feedback system are used to assess whether the control of NTMs on ITER is compatible with other simulataneous functionalities of the EC system, like core heating and current profile tailoring, or sawtooth control. Furthermore, results indicate that the power needs for control can be reduced if the EC power is reserved and if pre-emptive control is used as opposed to an active search for an already developed island.

EC power management in ITER for NTM control: the path from the commissioning phase to demonstration discharges

DOE PAGES

Poli, Francesca M.; Fredrickson, Eric; Henderson, Mark A.; ...

2017-10-23

Time dependent simulations that evolve consistently the magnetic equilibrium and plasma pressure profiles and the width and frequency rotation of magnetic islands under the effect of the Electron Cyclotron feedback system are used to assess whether the control of NTMs on ITER is compatible with other simulataneous functionalities of the EC system, like core heating and current profile tailoring, or sawtooth control. Furthermore, results indicate that the power needs for control can be reduced if the EC power is reserved and if pre-emptive control is used as opposed to an active search for an already developed island.

Scale-Up: Improving Large Enrollment Physics Courses

NASA Astrophysics Data System (ADS)

Beichner, Robert

1999-11-01

The Student-Centered Activities for Large Enrollment University Physics (SCALE-UP) project is working to establish a learning environment that will promote increased conceptual understanding, improved problem-solving performance, and greater student satisfaction, while still maintaining class sizes of approximately 100. We are also addressing the new ABET engineering accreditation requirements for inquiry-based learning along with communication and team-oriented skills development. Results of studies of our latest classroom design, plans for future classroom space, and the current iteration of instructional materials will be discussed.

Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion.

PubMed

Wang, Yang; Zhang, Wenjie; Wu, Lin; Lin, Xuemin; Zhao, Xiang

2017-01-01

Learning an ideal metric is crucial to many tasks in computer vision. Diverse feature representations may combat this problem from different aspects; as visual data objects described by multiple features can be decomposed into multiple views, thus often provide complementary information. In this paper, we propose a cross-view fusion algorithm that leads to a similarity metric for multiview data by systematically fusing multiple similarity measures. Unlike existing paradigms, we focus on learning distance measure by exploiting a graph structure of data samples, where an input similarity matrix can be improved through a propagation of graph random walk. In particular, we construct multiple graphs with each one corresponding to an individual view, and a cross-view fusion approach based on graph random walk is presented to derive an optimal distance measure by fusing multiple metrics. Our method is scalable to a large amount of data by enforcing sparsity through an anchor graph representation. To adaptively control the effects of different views, we dynamically learn view-specific coefficients, which are leveraged into graph random walk to balance multiviews. However, such a strategy may lead to an over-smooth similarity metric where affinities between dissimilar samples may be enlarged by excessively conducting cross-view fusion. Thus, we figure out a heuristic approach to controlling the iteration number in the fusion process in order to avoid over smoothness. Extensive experiments conducted on real-world data sets validate the effectiveness and efficiency of our approach.

Input-output-controlled nonlinear equation solvers

NASA Technical Reports Server (NTRS)

Padovan, Joseph

1988-01-01

To upgrade the efficiency and stability of the successive substitution (SS) and Newton-Raphson (NR) schemes, the concept of input-output-controlled solvers (IOCS) is introduced. By employing the formal properties of the constrained version of the SS and NR schemes, the IOCS algorithm can handle indefiniteness of the system Jacobian, can maintain iterate monotonicity, and provide for separate control of load incrementation and iterate excursions, as well as having other features. To illustrate the algorithmic properties, the results for several benchmark examples are presented. These define the associated numerical efficiency and stability of the IOCS.

Decentralized Control of Sound Radiation from an Aircraft-Style Panel Using Iterative Loop Recovery

NASA Technical Reports Server (NTRS)

Schiller, Noah H.; Cabell, Randolph H.; Fuller, Chris R.

2008-01-01

A decentralized LQG-based control strategy is designed to reduce low-frequency sound transmission through periodically stiffened panels. While modern control strategies have been used to reduce sound radiation from relatively simple structural acoustic systems, significant implementation issues have to be addressed before these control strategies can be extended to large systems such as the fuselage of an aircraft. For instance, centralized approaches typically require a high level of connectivity and are computationally intensive, while decentralized strategies face stability problems caused by the unmodeled interaction between neighboring control units. Since accurate uncertainty bounds are not known a priori, it is difficult to ensure the decentralized control system will be robust without making the controller overly conservative. Therefore an iterative approach is suggested, which utilizes frequency-shaped loop recovery. The approach accounts for modeling error introduced by neighboring control loops, requires no communication between subsystems, and is relatively simple. The control strategy is validated using real-time control experiments performed on a built-up aluminum test structure representative of the fuselage of an aircraft. Experiments demonstrate that the iterative approach is capable of achieving 12 dB peak reductions and a 3.6 dB integrated reduction in radiated sound power from the stiffened panel.

Service-Learning in the Environmental Sciences for Teaching Sustainability Science

NASA Astrophysics Data System (ADS)

Truebe, S.; Strong, A. L.

2016-12-01

Understanding and developing effective strategies for the use of community-engaged learning (service-learning) approaches in the environmental geosciences is an important research need in curricular and pedagogical innovation for sustainability. In 2015, we designed and implemented a new community-engaged learning practicum course through the Earth Systems Program in the School of Earth, Energy and Environmental Sciences at Stanford University focused on regional open space management and land stewardship. Undergraduate and graduate students partnered with three different regional land trust and environmental stewardship organizations to conduct quarter-long research projects ranging from remote sensing studies of historical land use, to fire ecology, to ranchland management, to volunteer retention strategies. Throughout the course, students reflected on the decision-making processes and stewardship actions of the organizations. Two iterations of the course were run in Winter and Fall 2015. Using coded and analyzed pre- and post-course student surveys from the two course iterations, we evaluate undergraduate and graduate student learning outcomes and changes in perceptions and understanding of sustainability science. We find that engagement with community partners to conduct research projects on a wide variety of aspects of open space management, land management, and environmental stewardship (1) increased an understanding of trade-offs inherent in sustainability and resource management and (2) altered student perceptions of the role of scientific information and research in environmental management and decision-making. Furthermore, students initially conceived of open space as purely ecological/biophysical, but by the end of the course, (3) their understanding was of open space as a coupled human/ecological system. This shift is crucial for student development as sustainability scientists.

Citizen science in natural resources: Lessons learned from stakeholder engagement in participatory research using collaborative adaptive management

USDA-ARS?s Scientific Manuscript database

Under the traditional “loading-dock” model of research, stakeholders are involved in determining priorities prior to research activities and then recieve one-way communication about findings after research is completed. This approach lacks iterative engagement of stakeholders during the research pro...

Fostering 21st Century Skills through Game Design and Development

ERIC Educational Resources Information Center

Garvey, Gregory P.

2015-01-01

This reflection paper argues that the design and development of digital games teach essential 21st century skills. Intrinsic to application and game development is design thinking. Design thinking requires iterative development, which demands creativity, critical thinking and problem solving. Students are engaged through learning by doing in both…

Using POGIL to Help Students Learn to Program

ERIC Educational Resources Information Center

Hu, Helen H.; Shepherd, Tricia D.

2013-01-01

POGIL has been successfully implemented in a scientific computing course to teach science students how to program in Python. Following POGIL guidelines, the authors have developed guided inquiry activities that lead student teams to discover and understand programming concepts. With each iteration of the scientific computing course, the authors…

Teaching Engineering Design Through Paper Rockets

ERIC Educational Resources Information Center

Welling, Jonathan; Wright, Geoffrey A.

2018-01-01

The paper rocket activity described in this article effectively teaches the engineering design process (EDP) by engaging students in a problem-based learning activity that encourages iterative design. For example, the first rockets the students build typically only fly between 30 and 100 feet. As students test and evaluate their rocket designs,…

Changing Mathematics Teachers' Conceptions of Assessment and Feedback

ERIC Educational Resources Information Center

Dixon, Helen; Haigh, Mavis

2009-01-01

Informed by an amalgam of notions drawn from constructivist, socio-cultural, metacognitive and self-regulation theory, the discourse created to describe assessment to enhance learning has gone through a number of iterations or discursive shifts. As a result, the current discourse is both ambitious and complex with the roles and responsibilities…

Design Experimentation with Multiple Perspectives: The GenScope Assessment Project.

ERIC Educational Resources Information Center

Hickey, Daniel T.; Kruger, Ann Cale; Frederick, Laura D.; Schafer, Nancy Jo; Zuiker, Steven

The GenScope Assessment Project is studying assessment in the context of a month-long computer-supported learning environment for introductory genetics. Across three annual iterations with multiple teachers, project researchers manipulated the materials, incentives, and contexts in which students were invited to use formative feedback on…

Seeing the Unseen Learner: Designing and Using Social Media to Recognize Children's Science Dispositions in Action

ERIC Educational Resources Information Center

Ahn, June; Clegg, Tamara; Yip, Jason; Bonsignore, Elizabeth; Pauw, Daniel; Gubbels, Michael; Lewittes, Charley; Rhodes, Emily

2016-01-01

This paper describes the development of "ScienceKit," a mobile, social media application to promote children's scientific inquiry. We deployed "ScienceKit" in "Kitchen Chemistry" ("KC"), an informal science program where children learn about scientific inquiry through cooking. By iteratively integrating…

Beyond the Given: Stretching Mathematical Thinking through Journaling

ERIC Educational Resources Information Center

Hamdan, May

2017-01-01

This paper reflects on a teaching experiment and presents details of a pedagogical technique where students periodically write down their clearest interpretation of the conditions imposed on various discrete mathematical subjects they are learning in class. Through iterations, students generate a more complete understanding of the material, one…

All the World's a Stage: Transforming Entrepreneurship Education through Design Thinking

ERIC Educational Resources Information Center

Huq, Afreen; Gilbert, David

2017-01-01

Purpose: The purpose of this paper is to present an alternate approach to entrepreneurship pedagogy development through an iterative journey of co-ownership between students, industry partners and academic course teams to enhance student satisfaction and learning outcomes. Design/methodology/approach: Utilising design thinking, the pedagogy…

An Online Image Analysis Tool for Science Education

ERIC Educational Resources Information Center

Raeside, L.; Busschots, B.; Waddington, S.; Keating, J. G.

2008-01-01

This paper describes an online image analysis tool developed as part of an iterative, user-centered development of an online Virtual Learning Environment (VLE) called the Education through Virtual Experience (EVE) Portal. The VLE provides a Web portal through which schoolchildren and their teachers create scientific proposals, retrieve images and…

Classroom Strategies to Make Sense and Persevere

ERIC Educational Resources Information Center

Wilburne, Jane M.; Wildmann, Tara; Morret, Michael; Stipanovic, Julie

2014-01-01

Three mid-level mathematics teachers (grades 7 and 8) and a university mathematics educator formed a year-long professional learning community. The objective was to collectively look at how they were promoting the Standards for Mathematical Practice (SMP) (CCSSI 2010) in their classes. The monthly discussions followed an iterative cycle in which…

Visualizing Community: Understanding Narrative Inquiry as Action Research

ERIC Educational Resources Information Center

Caine, Vera

2010-01-01

Throughout the school year I invited children in a Grade Two/Three learning strategies classroom to participate in a visual narrative inquiry. The intention was to explore children's knowledge of community in artful ways; the children photographed and wrote in what was often an iterative process, where writing/talking and photographing…

Teaching Game Sense in Soccer

ERIC Educational Resources Information Center

Pill, Shane

2012-01-01

"Game sense" is a sport-specific iteration of the teaching games for understanding model, designed to balance physical development of motor skill and fitness with the development of game understanding. Game sense can foster a shared vision for sport learning that bridges school physical education and community sport. This article explains how to…

Knowledge Sharing through Pair Programming in Learning Environments: An Empirical Study

ERIC Educational Resources Information Center

Kavitha, R. K.; Ahmed, M. S.

2015-01-01

Agile software development is an iterative and incremental methodology, where solutions evolve from self-organizing, cross-functional teams. Pair programming is a type of agile software development technique where two programmers work together with one computer for developing software. This paper reports the results of the pair programming…

Strategic by Design: Iterative Approaches to Educational Planning

ERIC Educational Resources Information Center

Chance, Shannon

2010-01-01

Linear planning and decision-making models assume a level of predictability that is uncommon today. Such models inadequately address the complex variables found in higher education. When academic organizations adopt paired-down business strategies, they restrict their own vision. They fail to harness emerging opportunities or learn from their own…

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Patient adaptive control of end-effector based gait rehabilitation devices using a haptic control framework.

PubMed

Hussein, Sami; Kruger, Jörg

2011-01-01

Robot assisted training has proven beneficial as an extension of conventional therapy to improve rehabilitation outcome. Further facilitation of this positive impact is expected from the application of cooperative control algorithms to increase the patient's contribution to the training effort according to his level of ability. This paper presents an approach for cooperative training for end-effector based gait rehabilitation devices. Thereby it provides the basis to firstly establish sophisticated cooperative control methods in this class of devices. It uses a haptic control framework to synthesize and render complex, task specific training environments, which are composed of polygonal primitives. Training assistance is integrated as part of the environment into the haptic control framework. A compliant window is moved along a nominal training trajectory compliantly guiding and supporting the foot motion. The level of assistance is adjusted via the stiffness of the moving window. Further an iterative learning algorithm is used to automatically adjust this assistance level. Stable haptic rendering of the dynamic training environments and adaptive movement assistance have been evaluated in two example training scenarios: treadmill walking and stair climbing. Data from preliminary trials with one healthy subject is provided in this paper. © 2011 IEEE

Vitamin D Requirements for the Future-Lessons Learned and Charting a Path Forward.

PubMed

Cashman, Kevin D

2018-04-25

Estimates of dietary requirements for vitamin D or Dietary Reference Values (DRV) are crucial from a public health perspective in providing a framework for prevention of vitamin D deficiency and optimizing vitamin D status of individuals. While these important public health policy instruments were developed with the evidence-base and data available at the time, there are some issues that need to be clarified or considered in future iterations of DRV for vitamin D. This is important as it will allow for more fine-tuned and truer estimates of the dietary requirements for vitamin D and thus provide for more population protection. The present review will overview some of the confusion that has arisen in relation to the application and/or interpretation of the definitions of the Estimated Average Requirement (EAR) and Recommended Dietary Allowance (RDA). It will also highlight some of the clarifications needed and, in particular, how utilization of a new approach in terms of using individual participant-level data (IPD), over and beyond aggregated data, from randomised controlled trials with vitamin D may have a key role in generating these more fine-tuned and truer estimates, which is of importance as we move towards the next iteration of vitamin D DRVs.

Description of the prototype diagnostic residual gas analyzer for ITER.

PubMed

Younkin, T R; Biewer, T M; Klepper, C C; Marcus, C

2014-11-01

The diagnostic residual gas analyzer (DRGA) system to be used during ITER tokamak operation is being designed at Oak Ridge National Laboratory to measure fuel ratios (deuterium and tritium), fusion ash (helium), and impurities in the plasma. The eventual purpose of this instrument is for machine protection, basic control, and physics on ITER. Prototyping is ongoing to optimize the hardware setup and measurement capabilities. The DRGA prototype is comprised of a vacuum system and measurement technologies that will overlap to meet ITER measurement requirements. Three technologies included in this diagnostic are a quadrupole mass spectrometer, an ion trap mass spectrometer, and an optical penning gauge that are designed to document relative and absolute gas concentrations.

[Arterial bypass iterative thrombosis and cancer: three cases].

PubMed

Villemur, B; Payraud, E; Seetha, V; De Angelis, M-P; Magne, J L; Perennou, D; Carpentier, P; Pernod, G

2014-02-01

Cancer associated with venous thromboembolic disease has been recognized since Trousseau, but a link between cancer and iterative arterial thrombosis is rarely described. We report three cases of patients with iterative bypass thrombosis in whom cancer was subsequently diagnosed: lung cancer in one patient and hepatocarcinoma and bladder cancer in the others. Smoking and hypertension were risk factors in both patients. The link between arterial thrombosis and cancer is probably multifactorial. In case of iterative arterial bypass thrombosis, the search for cancer is as useful as the control of cardiovascular risk factors and the search for antiphospholipid syndrome, since patient management can be affected. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Development of an Online Smartphone-Based eLearning Nutrition Education Program for Low-Income Individuals.

PubMed

Stotz, Sarah; Lee, Jung Sun

2018-01-01

The objective of this report was to describe the development process of an innovative smartphone-based electronic learning (eLearning) nutrition education program targeted to Supplemental Nutrition Assistance Program-Education-eligible individuals, entitled Food eTalk. Lessons learned from the Food eTalk development process suggest that it is critical to include all key team members from the program's inception using effective inter-team communication systems, understand the unique resources needed, budget ample time for development, and employ an iterative development and evaluation model. These lessons have implications for researchers and funding agencies in developing an innovative evidence-based eLearning nutrition education program to an increasingly technology-savvy, low-income audience. Copyright © 2016 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Teaching and learning recursive programming: a review of the research literature

NASA Astrophysics Data System (ADS)

McCauley, Renée; Grissom, Scott; Fitzgerald, Sue; Murphy, Laurie

2015-01-01

Hundreds of articles have been published on the topics of teaching and learning recursion, yet fewer than 50 of them have published research results. This article surveys the computing education research literature and presents findings on challenges students encounter in learning recursion, mental models students develop as they learn recursion, and best practices in introducing recursion. Effective strategies for introducing the topic include using different contexts such as recurrence relations, programming examples, fractal images, and a description of how recursive methods are processed using a call stack. Several studies compared the efficacy of introducing iteration before recursion and vice versa. The paper concludes with suggestions for future research into how students learn and understand recursion, including a look at the possible impact of instructor attitude and newer pedagogies.

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

Bilingual asynchronous online discussion groups: design and delivery of an eLearning distance study module for nurse academics in a developing country.

PubMed

Lewis, Peter A; Mai, Van Anh Thi; Gray, Genevieve

2012-04-01

The advent of eLearning has seen online discussion forums widely used in both undergraduate and postgraduate nursing education. This paper reports an Australian university experience of design, delivery and redevelopment of a distance education module developed for Vietnamese nurse academics. The teaching experience of Vietnamese nurse academics is mixed and frequently limited. It was decided that the distance module should attempt to utilise the experience of senior Vietnamese nurse academics - asynchronous online discussion groups were used to facilitate this. Online discussion occurred in both Vietnamese and English and was moderated by an Australian academic working alongside a Vietnamese translator. This paper will discuss the design of an online learning environment for foreign correspondents, the resources and translation required to maximise the success of asynchronous online discussion groups, as well as the rationale of delivering complex content in a foreign language. While specifically addressing the first iteration of the first distance module designed, this paper will also address subsequent changes made for the second iteration of the module and comment on their success. While a translator is clearly a key component of success, the elements of simplicity and clarity combined with supportive online moderation must not be overlooked. Copyright © 2011 Elsevier Ltd. All rights reserved.

Active Player Modeling in the Iterated Prisoner's Dilemma

PubMed Central

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions. PMID:26989405

Active Player Modeling in the Iterated Prisoner's Dilemma.

PubMed

Park, Hyunsoo; Kim, Kyung-Joong

2016-01-01

The iterated prisoner's dilemma (IPD) is well known within the domain of game theory. Although it is relatively simple, it can also elucidate important problems related to cooperation and trust. Generally, players can predict their opponents' actions when they are able to build a precise model of their behavior based on their game playing experience. However, it is difficult to make such predictions based on a limited number of games. The creation of a precise model requires the use of not only an appropriate learning algorithm and framework but also a good dataset. Active learning approaches have recently been introduced to machine learning communities. The approach can usually produce informative datasets with relatively little effort. Therefore, we have proposed an active modeling technique to predict the behavior of IPD players. The proposed method can model the opponent player's behavior while taking advantage of interactive game environments. This experiment used twelve representative types of players as opponents, and an observer used an active modeling algorithm to model these opponents. This observer actively collected data and modeled the opponent's behavior online. Most of our data showed that the observer was able to build, through direct actions, a more accurate model of an opponent's behavior than when the data were collected through random actions.

Temporal neural networks and transient analysis of complex engineering systems

NASA Astrophysics Data System (ADS)

Uluyol, Onder

A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

Produsage in a/synchronous learner-led e-learning

NASA Astrophysics Data System (ADS)

Kazmer, Michelle M.

2011-04-01

Creating a successful produsage environment for a required course taught via e-learning requires analyzing various factors: the learning context, learner-led education in required classes, the structure of the class, and reflections and evaluations of each semester's iteration of the course. Taking a produsage perspective, this paper analyzes the long-term development of a required graduate-level course in information organization. The course is examined closely to show how its materials, assignments, technology, instruction, and culture contribute to a learner-led produsage environment and lasting knowledge creation. The analysis leads to implications for course design and working with learners to create knowledge that may be applied in multiple settings.

Using Peer Feedback to Promote Reflection on Open-Ended Problems

NASA Astrophysics Data System (ADS)

Reinholz, Daniel L.; Dounas-Frazer, Dimitri R.

2016-09-01

This paper describes a new approach for learning from homework called Peer-Assisted Reflection (PAR). PAR involves students using peer feedback to improve their work on open-ended homework problems. Collaborating with peers and revising one's work based on the feedback of others are important aspects of doing and learning physics. While notable exceptions exist, homework and exams are generally individual activities that do not support collaboration and refinement, which misses important opportunities to use assessment for learning. In contrast, PAR provides students with a structure to iteratively engage with challenging, open-ended problems and solicit the input of their peers to improve their work.

Support patient search on pathology reports with interactive online learning based data extraction.

PubMed

Zheng, Shuai; Lu, James J; Appin, Christina; Brat, Daniel; Wang, Fusheng

2015-01-01

Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

A Real-Time Data Acquisition and Processing Framework Based on FlexRIO FPGA and ITER Fast Plant System Controller

NASA Astrophysics Data System (ADS)

Yang, C.; Zheng, W.; Zhang, M.; Yuan, T.; Zhuang, G.; Pan, Y.

2016-06-01

Measurement and control of the plasma in real-time are critical for advanced Tokamak operation. It requires high speed real-time data acquisition and processing. ITER has designed the Fast Plant System Controllers (FPSC) for these purposes. At J-TEXT Tokamak, a real-time data acquisition and processing framework has been designed and implemented using standard ITER FPSC technologies. The main hardware components of this framework are an Industrial Personal Computer (IPC) with a real-time system and FlexRIO devices based on FPGA. With FlexRIO devices, data can be processed by FPGA in real-time before they are passed to the CPU. The software elements are based on a real-time framework which runs under Red Hat Enterprise Linux MRG-R and uses Experimental Physics and Industrial Control System (EPICS) for monitoring and configuring. That makes the framework accord with ITER FPSC standard technology. With this framework, any kind of data acquisition and processing FlexRIO FPGA program can be configured with a FPSC. An application using the framework has been implemented for the polarimeter-interferometer diagnostic system on J-TEXT. The application is able to extract phase-shift information from the intermediate frequency signal produced by the polarimeter-interferometer diagnostic system and calculate plasma density profile in real-time. Different algorithms implementations on the FlexRIO FPGA are compared in the paper.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

USGS Publications Warehouse

Dietze, Michael C.; Fox, Andrew; Beck-Johnson, Lindsay; Betancourt, Julio L.; Hooten, Mevin B.; Jarnevich, Catherine S.; Keitt, Timothy H.; Kenney, Melissa A.; Laney, Christine M.; Larsen, Laurel G.; Loescher, Henry W.; Lunch, Claire K.; Pijanowski, Bryan; Randerson, James T.; Read, Emily; Tredennick, Andrew T.; Vargas, Rodrigo; Weathers, Kathleen C.; White, Ethan P.

2018-01-01

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

PubMed

Dietze, Michael C; Fox, Andrew; Beck-Johnson, Lindsay M; Betancourt, Julio L; Hooten, Mevin B; Jarnevich, Catherine S; Keitt, Timothy H; Kenney, Melissa A; Laney, Christine M; Larsen, Laurel G; Loescher, Henry W; Lunch, Claire K; Pijanowski, Bryan C; Randerson, James T; Read, Emily K; Tredennick, Andrew T; Vargas, Rodrigo; Weathers, Kathleen C; White, Ethan P

2018-02-13

Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Design Features of the Neutral Particle Diagnostic System for the ITER Tokamak

NASA Astrophysics Data System (ADS)

Petrov, S. Ya.; Afanasyev, V. I.; Melnik, A. D.; Mironov, M. I.; Navolotsky, A. S.; Nesenevich, V. G.; Petrov, M. P.; Chernyshev, F. V.; Kedrov, I. V.; Kuzmin, E. G.; Lyublin, B. V.; Kozlovski, S. S.; Mokeev, A. N.

2017-12-01

The control of the deuterium-tritium (DT) fuel isotopic ratio has to ensure the best performance of the ITER thermonuclear fusion reactor. The diagnostic system described in this paper allows the measurement of this ratio analyzing the hydrogen isotope fluxes (performing neutral particle analysis (NPA)). The development and supply of the NPA diagnostics for ITER was delegated to the Russian Federation. The diagnostics is being developed at the Ioffe Institute. The system consists of two analyzers, viz., LENPA (Low Energy Neutral Particle Analyzer) with 10-200 keV energy range and HENPA (High Energy Neutral Particle Analyzer) with 0.1-4.0MeV energy range. Simultaneous operation of both analyzers in different energy ranges enables researchers to measure the DT fuel ratio both in the central burning plasma (thermonuclear burn zone) and at the edge as well. When developing the diagnostic complex, it was necessary to account for the impact of several factors: high levels of neutron and gamma radiation, the direct vacuum connection to the ITER vessel, implying high tritium containment, strict requirements on reliability of all units and mechanisms, and the limited space available for accommodation of the diagnostic hardware at the ITER tokamak. The paper describes the design of the diagnostic complex and the engineering solutions that make it possible to conduct measurements under tokamak reactor conditions. The proposed engineering solutions provide a safe—with respect to thermal and mechanical loads—common vacuum channel for hydrogen isotope atoms to pass to the analyzers; ensure efficient shielding of the analyzers from the ITER stray magnetic field (up to 1 kG); provide the remote control of the NPA diagnostic complex, in particular, connection/disconnection of the NPA vacuum beamline from the ITER vessel; meet the ITER radiation safety requirements; and ensure measurements of the fuel isotopic ratio under high levels of neutron and gamma radiation.

Control of particle and power exhaust in pellet fuelled ITER DT scenarios employing integrated models

NASA Astrophysics Data System (ADS)

Wiesen, S.; Köchl, F.; Belo, P.; Kotov, V.; Loarte, A.; Parail, V.; Corrigan, G.; Garzotti, L.; Harting, D.

2017-07-01

The integrated model JINTRAC is employed to assess the dynamic density evolution of the ITER baseline scenario when fuelled by discrete pellets. The consequences on the core confinement properties, α-particle heating due to fusion and the effect on the ITER divertor operation, taking into account the material limitations on the target heat loads, are discussed within the integrated model. Using the model one can observe that stable but cyclical operational regimes can be achieved for a pellet-fuelled ITER ELMy H-mode scenario with Q  =  10 maintaining partially detached conditions in the divertor. It is shown that the level of divertor detachment is inversely correlated with the core plasma density due to α-particle heating, and thus depends on the density evolution cycle imposed by pellet ablations. The power crossing the separatrix to be dissipated depends on the enhancement of the transport in the pedestal region being linked with the pressure gradient evolution after pellet injection. The fuelling efficacy of the deposited pellet material is strongly dependent on the E  ×  B plasmoid drift. It is concluded that integrated models like JINTRAC, if validated and supported by realistic physics constraints, may help to establish suitable control schemes of particle and power exhaust in burning ITER DT-plasma scenarios.

EDITORIAL: ECRH physics and technology in ITER

NASA Astrophysics Data System (ADS)

Luce, T. C.

2008-05-01

It is a great pleasure to introduce you to this special issue containing papers from the 4th IAEA Technical Meeting on ECRH Physics and Technology in ITER, which was held 6-8 June 2007 at the IAEA Headquarters in Vienna, Austria. The meeting was attended by more than 40 ECRH experts representing 13 countries and the IAEA. Presentations given at the meeting were placed into five separate categories EC wave physics: current understanding and extrapolation to ITER Application of EC waves to confinement and stability studies, including active control techniques for ITER Transmission systems/launchers: state of the art and ITER relevant techniques Gyrotron development towards ITER needs System integration and optimisation for ITER. It is notable that the participants took seriously the focal point of ITER, rather than simply contributing presentations on general EC physics and technology. The application of EC waves to ITER presents new challenges not faced in the current generation of experiments from both the physics and technology viewpoints. High electron temperatures and the nuclear environment have a significant impact on the application of EC waves. The needs of ITER have also strongly motivated source and launcher development. Finally, the demonstrated ability for precision control of instabilities or non-inductive current drive in addition to bulk heating to fusion burn has secured a key role for EC wave systems in ITER. All of the participants were encouraged to submit their contributions to this special issue, subject to the normal publication and technical merit standards of Nuclear Fusion. Almost half of the participants chose to do so; many of the others had been published in other publications and therefore could not be included in this special issue. The papers included here are a representative sample of the meeting. The International Advisory Committee also asked the three summary speakers from the meeting to supply brief written summaries (O. Sauter: EC wave physics and applications, M. Thumm: Source and transmission line development, and S. Cirant: ITER specific system designs). These summaries are included in this issue to give a more complete view of the technical meeting. Finally, it is appropriate to mention the future of this meeting series. With the ratification of the ITER agreement and the formation of the ITER International Organization, it was recognized that meetings conducted by outside agencies with an exclusive focus on ITER would be somewhat unusual. However, the participants at this meeting felt that the gathering of international experts with diverse specialities within EC wave physics and technology to focus on using EC waves in future fusion devices like ITER was extremely valuable. It was therefore recommended that this series of meetings continue, but with the broader focus on the application of EC waves to steady-state and burning plasma experiments including demonstration power plants. As the papers in this special issue show, the EC community is already taking seriously the challenges of applying EC waves to fusion devices with high neutron fluence and continuous operation at high reliability.

A time-domain decomposition iterative method for the solution of distributed linear quadratic optimal control problems

NASA Astrophysics Data System (ADS)

Heinkenschloss, Matthias

2005-01-01

We study a class of time-domain decomposition-based methods for the numerical solution of large-scale linear quadratic optimal control problems. Our methods are based on a multiple shooting reformulation of the linear quadratic optimal control problem as a discrete-time optimal control (DTOC) problem. The optimality conditions for this DTOC problem lead to a linear block tridiagonal system. The diagonal blocks are invertible and are related to the original linear quadratic optimal control problem restricted to smaller time-subintervals. This motivates the application of block Gauss-Seidel (GS)-type methods for the solution of the block tridiagonal systems. Numerical experiments show that the spectral radii of the block GS iteration matrices are larger than one for typical applications, but that the eigenvalues of the iteration matrices decay to zero fast. Hence, while the GS method is not expected to convergence for typical applications, it can be effective as a preconditioner for Krylov-subspace methods. This is confirmed by our numerical tests.A byproduct of this research is the insight that certain instantaneous control techniques can be viewed as the application of one step of the forward block GS method applied to the DTOC optimality system.

Adaptive Statistical Iterative Reconstruction-V Versus Adaptive Statistical Iterative Reconstruction: Impact on Dose Reduction and Image Quality in Body Computed Tomography.

PubMed

Gatti, Marco; Marchisio, Filippo; Fronda, Marco; Rampado, Osvaldo; Faletti, Riccardo; Bergamasco, Laura; Ropolo, Roberto; Fonio, Paolo

The aim of this study was to evaluate the impact on dose reduction and image quality of the new iterative reconstruction technique: adaptive statistical iterative reconstruction (ASIR-V). Fifty consecutive oncologic patients acted as case controls undergoing during their follow-up a computed tomography scan both with ASIR and ASIR-V. Each study was analyzed in a double-blinded fashion by 2 radiologists. Both quantitative and qualitative analyses of image quality were conducted. Computed tomography scanner radiation output was 38% (29%-45%) lower (P < 0.0001) for the ASIR-V examinations than for the ASIR ones. The quantitative image noise was significantly lower (P < 0.0001) for ASIR-V. Adaptive statistical iterative reconstruction-V had a higher performance for the subjective image noise (P = 0.01 for 5 mm and P = 0.009 for 1.25 mm), the other parameters (image sharpness, diagnostic acceptability, and overall image quality) being similar (P > 0.05). Adaptive statistical iterative reconstruction-V is a new iterative reconstruction technique that has the potential to provide image quality equal to or greater than ASIR, with a dose reduction around 40%.

A Model For Selecting An Environmentally Responsive Trait: Evaluating Micro-scale Fitness Through UV-C Resistance and Exposure in Escherichia coli.

NASA Astrophysics Data System (ADS)

Schenone, D. J.; Igama, S.; Marash-Whitman, D.; Sloan, C.; Okansinski, A.; Moffet, A.; Grace, J. M.; Gentry, D.

2015-12-01

Experimental evolution of microorganisms in controlled microenvironments serves as a powerful tool for understanding the relationship between micro-scale microbial interactions as well as local-to global-scale environmental factors. In response to iterative and targeted environmental pressures, mutagenesis drives the emergence of novel phenotypes. Current methods to induce expression of these phenotypes require repetitive and time intensive procedures and do not allow for the continuous monitoring of conditions such as optical density, pH and temperature. To address this shortcoming, an Automated Dynamic Directed Evolution Chamber is being developed. It will initially produce Escherichia coli cells with an elevated UV-C resistance phenotype that will ultimately be adapted for different organisms as well as studying environmental effects. A useful phenotype and environmental factor for examining this relationship is UV-C resistance and exposure. In order to build a baseline for the device's operational parameters, a UV-C assay was performed on six E. coli replicates with three exposure fluxes across seven iterations. The fluxes included a 0 second exposure (control), 6 seconds at 3.3 J/m2/s and 40 seconds at 0.5 J/m2/s. After each iteration the cells were regrown and tested for UV-C resistance. We sought to quantify the increase and variability of UV-C resistance among different fluxes, and observe changes in each replicate at each iteration in terms of variance. Under different fluxes, we observed that the 0s control showed no significant increase in resistance, while the 6s/40s fluxes showed increased resistance as the number of iterations increased. A one-million fold increase in survivability was observed after seven iterations. Through statistical analysis using Spearman's rank correlation, the 40s exposure showed signs of more consistently increased resistance, but seven iterations was insufficient to demonstrate statistical significance; to test this further, our experiments will include more iterations. Furthermore, we plan to sequence all the replicants. As adaptation dynamics under intense UV exposure leads to high rate of change, it would be useful to observe differences in tolerance-related and non-tolerance-related genes between the original and UV resistant strains.

Controlling X-ray deformable mirrors during inspection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang, Lei; Xue, Junpeng; Idir, Mourad

2016-10-14

The X-ray deformable mirror (XDM) is becoming widely used in the present synchrotron/free-electron laser facilities because of its flexibility in correcting wavefront errors or modification of the beam size at the sample location. Owing to coupling among the N actuators of an XDM, (N + 1) or (2N + 1) scans are required to learn the response of each actuator one by one. When the mirror has an important number of actuators (N) and the actuator response time including stabilization or the necessary metrology time is long, the learning process can be time consuming. In this paper, a fast andmore » accurate method is presented to drive an XDM to a target shape usually with only three or four measurements during inspection. The metrology data are used as feedback to calculate the curvature discrepancy between the current and the target shapes. Three different derivative estimation methods are introduced to calculate the curvature from measured data. The mirror shape is becoming close to the target through iterative compensations. Finally, the feasibility of this simple and effective approach is demonstrated by a series of experiments.« less

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

PubMed

Zhang, Huaipin; Yue, Dong; Zhao, Wei; Hu, Songlin; Dou, Chunxia; Huaipin Zhang; Dong Yue; Wei Zhao; Songlin Hu; Chunxia Dou; Hu, Songlin; Zhang, Huaipin; Dou, Chunxia; Yue, Dong; Zhao, Wei

2018-06-01

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Improved Student Learning through a Faculty Learning Community: How Faculty Collaboration Transformed a Large-Enrollment Course from Lecture to Student Centered

PubMed Central

Elliott, Emily R.; Reason, Robert D.; Coffman, Clark R.; Gangloff, Eric J.; Raker, Jeffrey R.; Powell-Coffman, Jo Anne; Ogilvie, Craig A.

2016-01-01

Undergraduate introductory biology courses are changing based on our growing understanding of how students learn and rapid scientific advancement in the biological sciences. At Iowa State University, faculty instructors are transforming a second-semester large-enrollment introductory biology course to include active learning within the lecture setting. To support this change, we set up a faculty learning community (FLC) in which instructors develop new pedagogies, adapt active-learning strategies to large courses, discuss challenges and progress, critique and revise classroom interventions, and share materials. We present data on how the collaborative work of the FLC led to increased implementation of active-learning strategies and a concurrent improvement in student learning. Interestingly, student learning gains correlate with the percentage of classroom time spent in active-learning modes. Furthermore, student attitudes toward learning biology are weakly positively correlated with these learning gains. At our institution, the FLC framework serves as an agent of iterative emergent change, resulting in the creation of a more student-centered course that better supports learning. PMID:27252298

Total recall in distributive associative memories

NASA Technical Reports Server (NTRS)

Danforth, Douglas G.

1991-01-01

Iterative error correction of asymptotically large associative memories is equivalent to a one-step learning rule. This rule is the inverse of the activation function of the memory. Spectral representations of nonlinear activation functions are used to obtain the inverse in closed form for Sparse Distributed Memory, Selected-Coordinate Design, and Radial Basis Functions.

The Development of Instruments to Measure Motivational Interviewing Skill Acquisition for School-Based Personnel

ERIC Educational Resources Information Center

Small, Jason W.; Lee, Jon; Frey, Andy J.; Seeley, John R.; Walker, Hill M.

2014-01-01

As specialized instructional support personnel begin learning and using motivational interviewing (MI) techniques in school-based settings, there is growing need for context-specific measures to assess initial MI skill development. In this article, we describe the iterative development and preliminary evaluation of two measures of MI skill adapted…

On Adaptation, Maximization, and Reinforcement Learning among Cognitive Strategies

ERIC Educational Resources Information Center

Erev, Ido; Barron, Greg

2005-01-01

Analysis of binary choice behavior in iterated tasks with immediate feedback reveals robust deviations from maximization that can be described as indications of 3 effects: (a) a payoff variability effect, in which high payoff variability seems to move choice behavior toward random choice; (b) underweighting of rare events, in which alternatives…

Using Formative Assessment to Support Complex Learning in Conditions of Social Adversity

ERIC Educational Resources Information Center

Crossouard, Barbara

2011-01-01

This article reports on research into formative assessment within a task design that produces multiple opportunities for teacher and pupil dialogue. It draws upon in-depth case studies conducted in schools in socially deprived areas of Scotland, using policy and documentary analysis, video-observation, and an iterative series of interviews with…

Learning Contexts for Young Children in Chile: Process Quality Assessment in Preschool Centres

ERIC Educational Resources Information Center

Herrera, Maria Olivia; Mathiesen, Maria Elena; Merino, Jose Manuel; Recart, Isidora

2005-01-01

ITERS (Infant and Toddler Environment Rating Scale), ECERS (Early Childhood Environment Rating Scale) and SACERS (School Age Care Environment Rating Scale) are used to measure process quality. The psychometric characteristics of the three scales are established, and high reliability and adequate validity are observed. The global quality process…

Playing Modeling Games in the Science Classroom: The Case for Disciplinary Integration

ERIC Educational Resources Information Center

Sengupta, Pratim; Clark, Doug

2016-01-01

The authors extend the theory of "disciplinary integration" of games for science education beyond the virtual world of games, and identify two key themes of a practice-based theoretical commitment to science learning: (1) materiality in the classroom, and (2) iterative design of multiple, complementary, symbolic inscriptions (e.g.,…

Onwards and Upwards: Space, Placement, and Liminality in Adult ESOL Classes

ERIC Educational Resources Information Center

Baynham, Mike; Simpson, James

2010-01-01

The extensive literature on classroom-based second language learning makes little attempt to situate the classroom itself in social and multilingual sociolinguistic space, in the complex and iterative networks of encounters and interactions that make up daily life. Daily life is routinely evoked and "brought into" the classroom as a…

Panel Finds Few Learning Benefits in High-Stakes Exams

ERIC Educational Resources Information Center

Sparks, Sarah D.

2011-01-01

As Congress debates how to structure the next iteration of federal school accountability, a new national study has raised serious concerns about the effectiveness of test-based incentives to improve education. A blue-ribbon committee of the National Academies' National Research Council undertook a nearly decade-long study of test-based incentive…

iArchi[tech]ture: Developing a Mobile Social Media Framework for Pedagogical Transformation

ERIC Educational Resources Information Center

Cochrane, Thomas; Rhodes, David

2013-01-01

This paper critiques the journey of pedagogical change over three mobile learning (mlearning) project iterations (2009 to 2011) within the context of a Bachelor of Architecture degree. The three projects were supported by an intentional community of practice model involving a partnership of an educational researcher/technologist, course lecturers,…

The Evolution of Frequency Distributions: Relating Regularization to Inductive Biases through Iterated Learning

ERIC Educational Resources Information Center

Reali, Florencia; Griffiths, Thomas L.

2009-01-01

The regularization of linguistic structures by learners has played a key role in arguments for strong innate constraints on language acquisition, and has important implications for language evolution. However, relating the inductive biases of learners to regularization behavior in laboratory tasks can be challenging without a formal model. In this…

Exploring Teacher Leadership in a Rural, Secondary School: Reciprocal Learning Teams as a Catalyst for Emergent Leadership

ERIC Educational Resources Information Center

Cherkowski, Sabre; Schnellert, Leyton

2017-01-01

The purpose of this case study was to examine how teachers experienced professional development as collaborative inquiry, and how their experiences contributed to their development as teacher leaders. Three overarching themes were identified through iterative qualitative analysis of multiple data sources including interviews, observations,…

Graphic Design Education: A Revised Assessment Approach to Encourage Deep Learning

ERIC Educational Resources Information Center

Ellmers, Grant; Foley, Marius; Bennett, Sue

2008-01-01

In this paper we outline the review and iterative refinement of assessment procedures in a final year graphic design subject at the University of Wollongong. Our aim is to represent the main issues in assessing graphic design work, and informed by the literature, particularly "notions of creativity" (Cowdroy & de Graaff, 2005), to…

Research and Teaching: Development of Course-Based Undergraduate Research Experiences Using a Design-Based Approach

ERIC Educational Resources Information Center

Mordacq, John C.; Drane, Denise L.; Swarat, Su L.; Lo, Stanley M.

2017-01-01

In recent years, commissions and reports have called for laboratory courses that engage undergraduates in authentic research experiences. We present an iterative approach for developing course-based undergraduate research experiences (CUREs) that help students learn scientific inquiry skills and foster expert-like perceptions about biology. This…

Developing Vocabulary and Conceptual Knowledge for Low-Income Preschoolers: A Design Experiment

ERIC Educational Resources Information Center

Neuman, Susan B.; Dwyer, Julie

2011-01-01

The purpose of this design experiment was to research, test, and iteratively derive principles of word learning and word organization that could help to theoretically advance our understanding of vocabulary development for low-income preschoolers. Six Head Start teachers in morning and afternoon programs and their children (N = 89) were selected…

Teaching Assistant Competencies in Canada: Building a Framework for Practice Together

ERIC Educational Resources Information Center

Korpan, Cynthia; Sheffield, Suzanne Le-May; Verwoord, Roselynn

2015-01-01

This paper examines the stages of development for a framework of teaching assistant (TA) competencies initiated by the Teaching Assistant and Graduate Student Advancement (TAGSA) special interest group (SIG) of the Society of Teaching and Learning in Higher Education (STLHE). TAGSA initiated an iterative consultative process to inform the creation…

The Strategic Design Inquiry: A Formal Methodology For Approaching, Designing, Integrating, And Articulating National Strategy

DTIC Science & Technology

2014-04-01

15 Figure 4: Example cognitive map ... map , aligning planning efforts throughout the government. Even after strategy implementation, SDI calls for continuing, iterative learning and...the design before total commitment to it. Capturing this analysis on a cognitive map allows strategists to articulate a design to government

Negotiating Meaning in Cross-National Studies of Mathematics Teaching: Kissing Frogs to Find Princes

ERIC Educational Resources Information Center

Andrews, Paul

2007-01-01

This paper outlines the iterative processes by which a multinational team of researchers developed a low-inference framework for the analysis of video recordings of mathematics lessons drawn from Flemish Belgium, England, Finland, Hungary and Spain. Located within a theoretical framework concerning learning as the negotiation of meaning, we…

Makerspaces: The Next Iteration for Educational Technology in K-12 Schools

ERIC Educational Resources Information Center

Strycker, Jesse

2015-01-01

With the continually growing number of computers and mobile devices available in K-12 schools, the need is dwindling for dedicated computer labs and media centers. Some schools are starting to repurpose those facilities into different kinds of exploratory learning environments known as "makerspaces". This article discusses this next…

Introducing the Social Presence Model to Explore Online and Blended Learning Experiences

ERIC Educational Resources Information Center

Whiteside, Aimee L.

2015-01-01

This study explores the level of "social presence" or connectedness, in two iterations of a 13-month, graduate-level certificate program designed to help K-12 school leaders integrate technology in their districts. Vygotsky's Social Development Theory serves as the theoretical lens for this programmatic research. The methods include a…

Intrusion detection using rough set classification.

PubMed

Zhang, Lian-hua; Zhang, Guan-hua; Zhang, Jie; Bai, Ying-cai

2004-09-01

Recently machine learning-based intrusion detection approaches have been subjected to extensive researches because they can detect both misuse and anomaly. In this paper, rough set classification (RSC), a modern learning algorithm, is used to rank the features extracted for detecting intrusions and generate intrusion detection models. Feature ranking is a very critical step when building the model. RSC performs feature ranking before generating rules, and converts the feature ranking to minimal hitting set problem addressed by using genetic algorithm (GA). This is done in classical approaches using Support Vector Machine (SVM) by executing many iterations, each of which removes one useless feature. Compared with those methods, our method can avoid many iterations. In addition, a hybrid genetic algorithm is proposed to increase the convergence speed and decrease the training time of RSC. The models generated by RSC take the form of "IF-THEN" rules, which have the advantage of explication. Tests and comparison of RSC with SVM on DARPA benchmark data showed that for Probe and DoS attacks both RSC and SVM yielded highly accurate results (greater than 99% accuracy on testing set).

The U.S. Geological Survey Modular Ground-Water Model - PCGN: A Preconditioned Conjugate Gradient Solver with Improved Nonlinear Control

USGS Publications Warehouse

Naff, Richard L.; Banta, Edward R.

2008-01-01

The preconditioned conjugate gradient with improved nonlinear control (PCGN) package provides addi-tional means by which the solution of nonlinear ground-water flow problems can be controlled as compared to existing solver packages for MODFLOW. Picard iteration is used to solve nonlinear ground-water flow equations by iteratively solving a linear approximation of the nonlinear equations. The linear solution is provided by means of the preconditioned conjugate gradient algorithm where preconditioning is provided by the modi-fied incomplete Cholesky algorithm. The incomplete Cholesky scheme incorporates two levels of fill, 0 and 1, in which the pivots can be modified so that the row sums of the preconditioning matrix and the original matrix are approximately equal. A relaxation factor is used to implement the modified pivots, which determines the degree of modification allowed. The effects of fill level and degree of pivot modification are briefly explored by means of a synthetic, heterogeneous finite-difference matrix; results are reported in the final section of this report. The preconditioned conjugate gradient method is coupled with Picard iteration so as to efficiently solve the nonlinear equations associated with many ground-water flow problems. The description of this coupling of the linear solver with Picard iteration is a primary concern of this document.

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

PubMed

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Fundamental concepts of problem-based learning for the new facilitator.

PubMed Central

Kanter, S L

1998-01-01

Problem-based learning (PBL) is a powerful small group learning tool that should be part of the armamentarium of every serious educator. Classic PBL uses ill-structured problems to simulate the conditions that occur in the real environment. Students play an active role and use an iterative process of seeking new information based on identified learning issues, restructuring the information in light of the new knowledge, gathering additional information, and so forth. Faculty play a facilitatory role, not a traditional instructional role, by posing metacognitive questions to students. These questions serve to assist in organizing, generalizing, and evaluating knowledge; to probe for supporting evidence; to explore faulty reasoning; to stimulate discussion of attitudes; and to develop self-directed learning and self-assessment skills. Professional librarians play significant roles in the PBL environment extending from traditional service provider to resource person to educator. Students and faculty usually find the learning experience productive and enjoyable. PMID:9681175

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms

PubMed Central

Yang, Changju; Kim, Hyongsuk; Adhikari, Shyam Prasad; Chua, Leon O.

2016-01-01

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems. PMID:28025566

Error Control Coding Techniques for Space and Satellite Communications

NASA Technical Reports Server (NTRS)

Costello, Daniel J., Jr.; Takeshita, Oscar Y.; Cabral, Hermano A.; He, Jiali; White, Gregory S.

1997-01-01

Turbo coding using iterative SOVA decoding and M-ary differentially coherent or non-coherent modulation can provide an effective coding modulation solution: (1) Energy efficient with relatively simple SOVA decoding and small packet lengths, depending on BEP required; (2) Low number of decoding iterations required; and (3) Robustness in fading with channel interleaving.

Efficient full-chip SRAF placement using machine learning for best accuracy and improved consistency

NASA Astrophysics Data System (ADS)

Wang, Shibing; Baron, Stanislas; Kachwala, Nishrin; Kallingal, Chidam; Sun, Dezheng; Shu, Vincent; Fong, Weichun; Li, Zero; Elsaid, Ahmad; Gao, Jin-Wei; Su, Jing; Ser, Jung-Hoon; Zhang, Quan; Chen, Been-Der; Howell, Rafael; Hsu, Stephen; Luo, Larry; Zou, Yi; Zhang, Gary; Lu, Yen-Wen; Cao, Yu

2018-03-01

Various computational approaches from rule-based to model-based methods exist to place Sub-Resolution Assist Features (SRAF) in order to increase process window for lithography. Each method has its advantages and drawbacks, and typically requires the user to make a trade-off between time of development, accuracy, consistency and cycle time. Rule-based methods, used since the 90 nm node, require long development time and struggle to achieve good process window performance for complex patterns. Heuristically driven, their development is often iterative and involves significant engineering time from multiple disciplines (Litho, OPC and DTCO). Model-based approaches have been widely adopted since the 20 nm node. While the development of model-driven placement methods is relatively straightforward, they often become computationally expensive when high accuracy is required. Furthermore these methods tend to yield less consistent SRAFs due to the nature of the approach: they rely on a model which is sensitive to the pattern placement on the native simulation grid, and can be impacted by such related grid dependency effects. Those undesirable effects tend to become stronger when more iterations or complexity are needed in the algorithm to achieve required accuracy. ASML Brion has developed a new SRAF placement technique on the Tachyon platform that is assisted by machine learning and significantly improves the accuracy of full chip SRAF placement while keeping consistency and runtime under control. A Deep Convolutional Neural Network (DCNN) is trained using the target wafer layout and corresponding Continuous Transmission Mask (CTM) images. These CTM images have been fully optimized using the Tachyon inverse mask optimization engine. The neural network generated SRAF guidance map is then used to place SRAF on full-chip. This is different from our existing full-chip MB-SRAF approach which utilizes a SRAF guidance map (SGM) of mask sensitivity to improve the contrast of optical image at the target pattern edges. In this paper, we demonstrate that machine learning assisted SRAF placement can achieve a superior process window compared to the SGM model-based SRAF method, while keeping the full-chip runtime affordable, and maintain consistency of SRAF placement . We describe the current status of this machine learning assisted SRAF technique and demonstrate its application to full chip mask synthesis and discuss how it can extend the computational lithography roadmap.

The fractal geometry of Hartree-Fock

NASA Astrophysics Data System (ADS)

Theel, Friethjof; Karamatskou, Antonia; Santra, Robin

2017-12-01

The Hartree-Fock method is an important approximation for the ground-state electronic wave function of atoms and molecules so that its usage is widespread in computational chemistry and physics. The Hartree-Fock method is an iterative procedure in which the electronic wave functions of the occupied orbitals are determined. The set of functions found in one step builds the basis for the next iteration step. In this work, we interpret the Hartree-Fock method as a dynamical system since dynamical systems are iterations where iteration steps represent the time development of the system, as encountered in the theory of fractals. The focus is put on the convergence behavior of the dynamical system as a function of a suitable control parameter. In our case, a complex parameter λ controls the strength of the electron-electron interaction. An investigation of the convergence behavior depending on the parameter λ is performed for helium, neon, and argon. We observe fractal structures in the complex λ-plane, which resemble the well-known Mandelbrot set, determine their fractal dimension, and find that with increasing nuclear charge, the fragmentation increases as well.

Developing DIII-D To Prepare For ITER And The Path To Fusion Energy

NASA Astrophysics Data System (ADS)

Buttery, Richard; Hill, David; Solomon, Wayne; Guo, Houyang; DIII-D Team

2017-10-01

DIII-D pursues the advancement of fusion energy through scientific understanding and discovery of solutions. Research targets two key goals. First, to prepare for ITER we must resolve how to use its flexible control tools to rapidly reach Q =10, and develop the scientific basis to interpret results from ITER for fusion projection. Second, we must determine how to sustain a high performance fusion core in steady state conditions, with minimal actuators and a plasma exhaust solution. DIII-D will target these missions with: (i) increased electron heating and balanced torque neutral beams to simulate burning plasma conditions (ii) new 3D coil arrays to resolve control of transients (iii) off axis current drive to study physics in steady state regimes (iv) divertors configurations to promote detachment with low upstream density (v) a reactor relevant wall to qualify materials and resolve physics in reactor-like conditions. With new diagnostics and leading edge simulation, this will position the US for success in ITER and a unique knowledge to accelerate the approach to fusion energy. Supported by the US DOE under DE-FC02-04ER54698.

Design and FPGA Implementation of a Universal Chaotic Signal Generator Based on the Verilog HDL Fixed-Point Algorithm and State Machine Control

NASA Astrophysics Data System (ADS)

Qiu, Mo; Yu, Simin; Wen, Yuqiong; Lü, Jinhu; He, Jianbin; Lin, Zhuosheng

In this paper, a novel design methodology and its FPGA hardware implementation for a universal chaotic signal generator is proposed via the Verilog HDL fixed-point algorithm and state machine control. According to continuous-time or discrete-time chaotic equations, a Verilog HDL fixed-point algorithm and its corresponding digital system are first designed. In the FPGA hardware platform, each operation step of Verilog HDL fixed-point algorithm is then controlled by a state machine. The generality of this method is that, for any given chaotic equation, it can be decomposed into four basic operation procedures, i.e. nonlinear function calculation, iterative sequence operation, iterative values right shifting and ceiling, and chaotic iterative sequences output, each of which corresponds to only a state via state machine control. Compared with the Verilog HDL floating-point algorithm, the Verilog HDL fixed-point algorithm can save the FPGA hardware resources and improve the operation efficiency. FPGA-based hardware experimental results validate the feasibility and reliability of the proposed approach.

Low-cost autonomous perceptron neural network inspired by quantum computation

NASA Astrophysics Data System (ADS)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Adaptive management of watersheds and related resources

USGS Publications Warehouse

Williams, Byron K.

2009-01-01

The concept of learning about natural resources through the practice of management has been around for several decades and by now is associated with the term adaptive management. The objectives of this paper are to offer a framework for adaptive management that includes an operational definition, a description of conditions in which it can be usefully applied, and a systematic approach to its application. Adaptive decisionmaking is described as iterative, learning-based management in two phases, each with its own mechanisms for feedback and adaptation. The linkages between traditional experimental science and adaptive management are discussed.

Active Learning by Querying Informative and Representative Examples.

PubMed

Huang, Sheng-Jun; Jin, Rong; Zhou, Zhi-Hua

2014-10-01

Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.

Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

PubMed Central

Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Nie, Feiping; Munsell, Brent

2018-01-01

Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets. PMID:28551556

Electronic patient-reported data capture as a foundation of rapid learning cancer care.

PubMed

Abernethy, Amy P; Ahmad, Asif; Zafar, S Yousuf; Wheeler, Jane L; Reese, Jennifer Barsky; Lyerly, H Kim

2010-06-01

"Rapid learning healthcare" presents a new infrastructure to support comparative effectiveness research. By leveraging heterogeneous datasets (eg, clinical, administrative, genomic, registry, and research), health information technology, and sophisticated iterative analyses, rapid learning healthcare provides a real-time framework in which clinical studies can evaluate the relative impact of therapeutic approaches on a diverse array of measures. This article describes an effort, at 1 academic medical center, to demonstrate what rapid learning healthcare might look like in operation. The article describes the process of developing and testing the components of this new model of integrated clinical/research function, with the pilot site being an academic oncology clinic and with electronic patient-reported outcomes (ePROs) being the foundational dataset. Steps included: feasibility study of the ePRO system; validation study of ePRO collection across 3 cancers; linking ePRO and other datasets; implementation; stakeholder alignment and buy in, and; demonstration through use cases. Two use cases are presented; participants were metastatic breast cancer (n = 65) and gastrointestinal cancer (n = 113) patients at 2 academic medical centers. (1) Patient-reported symptom data were collected with tablet computers; patients with breast and gastrointestinal cancer indicated high levels of sexual distress, which prompted multidisciplinary response, design of an intervention, and successful application for funding to study the intervention's impact. (2) The system evaluated the longitudinal impact of a psychosocial care program provided to patients with breast cancer. Participants used tablet computers to complete PRO surveys; data indicated significant impact on psychosocial outcomes, notably distress and despair, despite advanced disease. Results return to the clinic, allowing iterative update and evaluation. An ePRO-based rapid learning cancer clinic is feasible, providing real-time research-quality data to support comparative effectiveness research.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

ISS Double-Gimbaled CMG Subsystem Simulation Using the Agile Development Method

NASA Technical Reports Server (NTRS)

Inampudi, Ravi

2016-01-01

This paper presents an evolutionary approach in simulating a cluster of 4 Control Moment Gyros (CMG) on the International Space Station (ISS) using a common sense approach (the agile development method) for concurrent mathematical modeling and simulation of the CMG subsystem. This simulation is part of Training systems for the 21st Century simulator which will provide training for crew members, instructors, and flight controllers. The basic idea of how the CMGs on the space station are used for its non-propulsive attitude control is briefly explained to set up the context for simulating a CMG subsystem. Next different reference frames and the detailed equations of motion (EOM) for multiple double-gimbal variable-speed control moment gyroscopes (DGVs) are presented. Fixing some of the terms in the EOM becomes the special case EOM for ISS's double-gimbaled fixed speed CMGs. CMG simulation development using the agile development method is presented in which customer's requirements and solutions evolve through iterative analysis, design, coding, unit testing and acceptance testing. At the end of the iteration a set of features implemented in that iteration are demonstrated to the flight controllers thus creating a short feedback loop and helping in creating adaptive development cycles. The unified modeling language (UML) tool is used in illustrating the user stories, class designs and sequence diagrams. This incremental development approach of mathematical modeling and simulating the CMG subsystem involved the development team and the customer early on, thus improving the quality of the working CMG system in each iteration and helping the team to accurately predict the cost, schedule and delivery of the software.

Optimal Decentralized Protocol for Electric Vehicle Charging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gan, LW; Topcu, U; Low, SH

We propose a decentralized algorithm to optimally schedule electric vehicle (EV) charging. The algorithm exploits the elasticity of electric vehicle loads to fill the valleys in electric load profiles. We first formulate the EV charging scheduling problem as an optimal control problem, whose objective is to impose a generalized notion of valley-filling, and study properties of optimal charging profiles. We then give a decentralized algorithm to iteratively solve the optimal control problem. In each iteration, EVs update their charging profiles according to the control signal broadcast by the utility company, and the utility company alters the control signal to guidemore » their updates. The algorithm converges to optimal charging profiles (that are as "flat" as they can possibly be) irrespective of the specifications (e.g., maximum charging rate and deadline) of EVs, even if EVs do not necessarily update their charging profiles in every iteration, and use potentially outdated control signal when they update. Moreover, the algorithm only requires each EV solving its local problem, hence its implementation requires low computation capability. We also extend the algorithm to track a given load profile and to real-time implementation.« less

Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun

2017-03-01

H ∞ control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.

Real-Time Adaptive Color Segmentation by Neural Networks

NASA Technical Reports Server (NTRS)

Duong, Tuan A.

2004-01-01

Artificial neural networks that would utilize the cascade error projection (CEP) algorithm have been proposed as means of autonomous, real-time, adaptive color segmentation of images that change with time. In the original intended application, such a neural network would be used to analyze digitized color video images of terrain on a remote planet as viewed from an uninhabited spacecraft approaching the planet. During descent toward the surface of the planet, information on the segmentation of the images into differently colored areas would be updated adaptively in real time to capture changes in contrast, brightness, and resolution, all in an effort to identify a safe and scientifically productive landing site and provide control feedback to steer the spacecraft toward that site. Potential terrestrial applications include monitoring images of crops to detect insect invasions and monitoring of buildings and other facilities to detect intruders. The CEP algorithm is reliable and is well suited to implementation in very-large-scale integrated (VLSI) circuitry. It was chosen over other neural-network learning algorithms because it is better suited to realtime learning: It provides a self-evolving neural-network structure, requires fewer iterations to converge and is more tolerant to low resolution (that is, fewer bits) in the quantization of neural-network synaptic weights. Consequently, a CEP neural network learns relatively quickly, and the circuitry needed to implement it is relatively simple. Like other neural networks, a CEP neural network includes an input layer, hidden units, and output units (see figure). As in other neural networks, a CEP network is presented with a succession of input training patterns, giving rise to a set of outputs that are compared with the desired outputs. Also as in other neural networks, the synaptic weights are updated iteratively in an effort to bring the outputs closer to target values. A distinctive feature of the CEP neural network and algorithm is that each update of synaptic weights takes place in conjunction with the addition of another hidden unit, which then remains in place as still other hidden units are added on subsequent iterations. For a given training pattern, the synaptic weight between (1) the inputs and the previously added hidden units and (2) the newly added hidden unit is updated by an amount proportional to the partial derivative of a quadratic error function with respect to the synaptic weight. The synaptic weight between the newly added hidden unit and each output unit is given by a more complex function that involves the errors between the outputs and their target values, the transfer functions (hyperbolic tangents) of the neural units, and the derivatives of the transfer functions.

Computational methods of robust controller design for aerodynamic flutter suppression

NASA Technical Reports Server (NTRS)

Anderson, L. R.

1981-01-01

The development of Riccati iteration, a tool for the design and analysis of linear control systems is examined. First, Riccati iteration is applied to the problem of pole placement and order reduction in two-time scale control systems. Order reduction, yielding a good approximation to the original system, is demonstrated using a 16th order linear model of a turbofan engine. Next, a numerical method for solving the Riccati equation is presented and demonstrated for a set of eighth order random examples. A literature review of robust controller design methods follows which includes a number of methods for reducing the trajectory and performance index sensitivity in linear regulators. Lastly, robust controller design for large parameter variations is discussed.

A Technique for Transient Thermal Testing of Thick Structures

NASA Technical Reports Server (NTRS)

Horn, Thomas J.; Richards, W. Lance; Gong, Leslie

1997-01-01

A new open-loop heat flux control technique has been developed to conduct transient thermal testing of thick, thermally-conductive aerospace structures. This technique uses calibration of the radiant heater system power level as a function of heat flux, predicted aerodynamic heat flux, and the properties of an instrumented test article. An iterative process was used to generate open-loop heater power profiles prior to each transient thermal test. Differences between the measured and predicted surface temperatures were used to refine the heater power level command profiles through the iteration process. This iteration process has reduced the effects of environmental and test system design factors, which are normally compensated for by closed-loop temperature control, to acceptable levels. The final revised heater power profiles resulted in measured temperature time histories which deviated less than 25 F from the predicted surface temperatures.

The elements of a commercial human spaceflight safety reporting system

NASA Astrophysics Data System (ADS)

Christensen, Ian

2017-10-01

In its report on the SpaceShipTwo accident the National Transportation Safety Board (NTSB) included in its recommendations that the Federal Aviation Administration (FAA) ;in collaboration with the commercial spaceflight industry, continue work to implement a database of lessons learned from commercial space mishap investigations and encourage commercial space industry members to voluntarily submit lessons learned.; In its official response to the NTSB the FAA supported this recommendation and indicated it has initiated an iterative process to put into place a framework for a cooperative safety data sharing process including the sharing of lessons learned, and trends analysis. Such a framework is an important element of an overall commercial human spaceflight safety system.

Self-control with spiking and non-spiking neural networks playing games.

PubMed

Christodoulou, Chris; Banfield, Gaye; Cleanthous, Aristodemos

2010-01-01

Self-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the payoffs for the dilemma cases in the IPD payoff matrix are differentially biased (increased or decreased), it is shown that increasing the precommitment effect (through increasing the differential bias) increases the probability of cooperating with oneself in the future, irrespective of whether the implementation is with spiking or non-spiking neural networks. Copyright (c) 2009 Elsevier Ltd. All rights reserved.

Interactive learning in 2×2 normal form games by neural network agents

NASA Astrophysics Data System (ADS)

Spiliopoulos, Leonidas

2012-11-01

This paper models the learning process of populations of randomly rematched tabula rasa neural network (NN) agents playing randomly generated 2×2 normal form games of all strategic classes. This approach has greater external validity than the existing models in the literature, each of which is usually applicable to narrow subsets of classes of games (often a single game) and/or to fixed matching protocols. The learning prowess of NNs with hidden layers was impressive as they learned to play unique pure strategy equilibria with near certainty, adhered to principles of dominance and iterated dominance, and exhibited a preference for risk-dominant equilibria. In contrast, perceptron NNs were found to perform significantly worse than hidden layer NN agents and human subjects in experimental studies.

The ITER Neutral Beam Test Facility towards SPIDER operation

NASA Astrophysics Data System (ADS)

Toigo, V.; Dal Bello, S.; Gaio, E.; Luchetta, A.; Pasqualotto, R.; Zaccaria, P.; Bigi, M.; Chitarin, G.; Marcuzzi, D.; Pomaro, N.; Serianni, G.; Agostinetti, P.; Agostini, M.; Antoni, V.; Aprile, D.; Baltador, C.; Barbisan, M.; Battistella, M.; Boldrin, M.; Brombin, M.; Dalla Palma, M.; De Lorenzi, A.; Delogu, R.; De Muri, M.; Fellin, F.; Ferro, A.; Gambetta, G.; Grando, L.; Jain, P.; Maistrello, A.; Manduchi, G.; Marconato, N.; Pavei, M.; Peruzzo, S.; Pilan, N.; Pimazzoni, A.; Piovan, R.; Recchia, M.; Rizzolo, A.; Sartori, E.; Siragusa, M.; Spada, E.; Spagnolo, S.; Spolaore, M.; Taliercio, C.; Valente, M.; Veltri, P.; Zamengo, A.; Zaniol, B.; Zanotto, L.; Zaupa, M.; Boilson, D.; Graceffa, J.; Svensson, L.; Schunke, B.; Decamps, H.; Urbani, M.; Kushwah, M.; Chareyre, J.; Singh, M.; Bonicelli, T.; Agarici, G.; Garbuglia, A.; Masiello, A.; Paolucci, F.; Simon, M.; Bailly-Maitre, L.; Bragulat, E.; Gomez, G.; Gutierrez, D.; Mico, G.; Moreno, J.-F.; Pilard, V.; Chakraborty, A.; Baruah, U.; Rotti, C.; Patel, H.; Nagaraju, M. V.; Singh, N. P.; Patel, A.; Dhola, H.; Raval, B.; Fantz, U.; Fröschle, M.; Heinemann, B.; Kraus, W.; Nocentini, R.; Riedl, R.; Schiesko, L.; Wimmer, C.; Wünderlich, D.; Cavenago, M.; Croci, G.; Gorini, G.; Rebai, M.; Muraro, A.; Tardocchi, M.; Hemsworth, R.

2017-08-01

SPIDER is one of two projects of the ITER Neutral Beam Test Facility under construction in Padova, Italy, at the Consorzio RFX premises. It will have a 100 keV beam source with a full-size prototype of the radiofrequency ion source for the ITER neutral beam injector (NBI) and also, similar to the ITER diagnostic neutral beam, it is designed to operate with a pulse length of up to 3600 s, featuring an ITER-like magnetic filter field configuration (for high extraction of negative ions) and caesium oven (for high production of negative ions) layout as well as a wide set of diagnostics. These features will allow a reproduction of the ion source operation in ITER, which cannot be done in any other existing test facility. SPIDER realization is well advanced and the first operation is expected at the beginning of 2018, with the mission of achieving the ITER heating and diagnostic NBI ion source requirements and of improving its performance in terms of reliability and availability. This paper mainly focuses on the preparation of the first SPIDER operations—integration and testing of SPIDER components, completion and implementation of diagnostics and control and formulation of operation and research plan, based on a staged strategy.

Virtual patients: practical advice for clinical authors using Labyrinth.

PubMed

Begg, Michael

2010-09-01

Labyrinth is a tool originally developed in the University of Edinburgh's Learning Technology Section for authoring and delivering branching case scenarios. The scenarios can incorporate game-informed elements such as scoring, randomising, avatars and counters. Labyrinth has grown more popular internationally since a version of the build was made available on the open source network Source Forge. This paper offers help and advice for clinical educators interested in creating cases. Labyrinth is increasingly recognised as a tool offering great potential for delivering cases that promote rich, situated learning opportunities for learners. There are, however, significant challenges to generating such cases, not least of which is the challenge for potential authors in approaching the process of constructing narrative-rich, context-sensitive cases in an unfamiliar authoring environment. This paper offers a brief overview of the principles informing Labyrinth cases (game-informed learning), and offers some practical advice to better prepare educators with little or no prior experience. Labyrinth has continued to grow and develop, from its roots as a research and development environment to one that is optimised for use by non-technical clinical educators. The process becomes increasingly iterative and better informed as the teaching community push the software further. The positive implications of providing practical advice and concept insight to new case authors is that it ideally leads to a broader base of users who will inform future iterations of the software. © Blackwell Publishing Ltd 2010.

MAP: an iterative experimental design methodology for the optimization of catalytic search space structure modeling.

PubMed

Baumes, Laurent A

2006-01-01

One of the main problems in high-throughput research for materials is still the design of experiments. At early stages of discovery programs, purely exploratory methodologies coupled with fast screening tools should be employed. This should lead to opportunities to find unexpected catalytic results and identify the "groups" of catalyst outputs, providing well-defined boundaries for future optimizations. However, very few new papers deal with strategies that guide exploratory studies. Mostly, traditional designs, homogeneous covering, or simple random samplings are exploited. Typical catalytic output distributions exhibit unbalanced datasets for which an efficient learning is hardly carried out, and interesting but rare classes are usually unrecognized. Here is suggested a new iterative algorithm for the characterization of the search space structure, working independently of learning processes. It enhances recognition rates by transferring catalysts to be screened from "performance-stable" space zones to "unsteady" ones which necessitate more experiments to be well-modeled. The evaluation of new algorithm attempts through benchmarks is compulsory due to the lack of past proofs about their efficiency. The method is detailed and thoroughly tested with mathematical functions exhibiting different levels of complexity. The strategy is not only empirically evaluated, the effect or efficiency of sampling on future Machine Learning performances is also quantified. The minimum sample size required by the algorithm for being statistically discriminated from simple random sampling is investigated.

Teaching hydrogeology: a review of current practice

NASA Astrophysics Data System (ADS)

Gleeson, T.; Allen, D. M.; Ferguson, G.

2012-07-01

Hydrogeology is now taught in a broad spectrum of departments and institutions to students with diverse backgrounds. Successful instruction in hydrogeology thus requires a variety of pedagogical approaches depending on desired learning outcomes and the background of students. We review the pedagogical literature in hydrogeology to highlight recent advances and analyze a 2005 survey among 68 hydrogeology instructors. The literature and survey results suggest there are only ~ 15 topics that are considered crucial by most hydrogeologists and > 100 other topics that are considered crucial by some hydrogeologists. The crucial topics focus on properties of aquifers and fundamentals of groundwater flow, and should likely be part of all undergraduate hydrogeology courses. Other topics can supplement and support these crucial topics, depending on desired learning outcomes. Classroom settings continue to provide a venue for emphasizing fundamental knowledge. However, recent pedagogical advances are biased towards field and laboratory instruction with a goal of bolstering experiential learning. Field methods build on the fundamentals taught in the classroom and emphasize the collection of data, data uncertainty, and the development of vocational skills. Laboratory and computer-based exercises similarly build on theory, and offer an opportunity for data analysis and integration. The literature suggests curricula at all levels should ideally balance field, laboratory, and classroom pedagogy into an iterative and integrative whole. An integrated, iterative and balanced approach leads to greater student motivation and advancement of theoretical and vocational knowledge.

An Iterative Learning Algorithm to Map Oil Palm Plantations from Synthetic Aperture Radar and Crowdsourcing

NASA Astrophysics Data System (ADS)

Pinto, N.; Zhang, Z.; Perger, C.; Aguilar-Amuchastegui, N.; Almeyda Zambrano, A. M.; Broadbent, E. N.; Simard, M.; Banerjee, S.

2017-12-01

The oil palm Elaeis spp. grows exclusively in the tropics and provides 30% of the world's vegetable oil. While oil palm-derived biodiesel can reduce carbon emissions from fossil fuels, plantation establishment may be associated with peat fires and deforestation. The ability to monitor plantation establishment and their expansion over carbon-rich tropical forests is critical for quantifying the net impact of oil palm commodities on carbon fluxes. Our objective is to develop a robust methodology to map oil palm plantations in tropical biomes, based on Synthetic Aperture Radar (SAR) from Sentinel-1, ALOS/PALSAR2, and UAVSAR. The C- and L-band signal from these instruments are sensitive to vegetation parameters such as canopy volume, trunk shape, and trunk spatial arrangement, that are critical to differentiate crops from forests and native palms. Based on Bayesian statistics, the learning algorithm employed here adapts to growing knowledge as sites and trainning points are added. We will present an iterative approach wherein a model is initially built at the site with the most training points - in our case, Costa Rica. Model posteriors from Costa Rica, depicting polarimetric signatures of oil palm plantations, are then used as priors in a classification exercise taking place in South Kalimantan. Results are evaluated by local researchers using the LACO Wiki interface. All validation points, including missclassified sites, are used in an additional iteration to improve model results to >90% overall accuracy. We report on the impact of plantation age on polarimetric signatures, and we also compare model performance with and without L-band data.

A Fast, Minimalist Search Tool for Remote Sensing Data

NASA Astrophysics Data System (ADS)

Lynnes, C. S.; Macharrie, P. G.; Elkins, M.; Joshi, T.; Fenichel, L. H.

2005-12-01

We present a tool that emphasizes speed and simplicity in searching remotely sensed Earth Science data. The tool, nicknamed "Mirador" (Spanish for a scenic overlook), provides only four freetext search form fields, for Keywords, Location, Data Start and Data Stop. This contrasts with many current Earth Science search tools that offer highly structured interfaces in order to ensure precise, non-zero results. The disadvantages of the structured approach lie in its complexity and resultant learning curve, as well as the time it takes to formulate and execute the search, thus discouraging iterative discovery. On the other hand, the success of the basic Google search interface shows that many users are willing to forgo high search precision if the search process is fast enough to enable rapid iteration. Therefore, we employ several methods to increase the speed of search formulation and execution. Search formulation is expedited by the minimalist search form, with only one required field. Also, a gazetteer enables the use of geographic terms as shorthand for latitude/longitude coordinates. The search execution is accelerated by initially presenting dataset results (returned from a Google Mini appliance) with an estimated number of "hits" for each dataset based on the user's space-time constraints. The more costly file-level search is executed against a PostGres database only when the user "drills down", and then covering only the fraction of the time period needed to return the next page of results. The simplicity of the search form makes the tool easy to learn and use, and the speed of the searches enables an iterative form of data discovery.

Co-Mentoring: The Iterative Process of Learning about Self and "Becoming" Leaders

ERIC Educational Resources Information Center

Allison, Valerie A.; Ramirez, Laurie A.

2016-01-01

Two pre-tenured faculty members at dissimilar institutions found themselves in similar positions--both were assigned to administrative positions that they did not seek. This self-study is an investigation of their processes of becoming leaders and how they aligned and/or conflicted with their espoused beliefs. A review of the literature that…

Enhancement Process of Didactic Strategies in a Degree Course for Pre-Service Teachers

ERIC Educational Resources Information Center

Garcias, Adolfina Pérez; Marín, Victoria I.

2017-01-01

This paper presents a study on the enhancement of didactic strategies based on the idea of personal learning environments (PLE). It was conducted through three iterative cycles during three consecutive academic years according to the phases of design-based research applied to teaching in a university course for pre-service teachers in the…

Assessing Children's Understanding of Length Measurement: A Focus on Three Key Concepts

ERIC Educational Resources Information Center

Bush, Heidi

2009-01-01

In this article, the author presents three different tasks that can be used to assess students' understanding of the concept of length. Three important measurement concepts for students to understand are transitive reasoning, use of identical units, and iteration. In any teaching and learning process it is important to acknowledge students'…

Using Performance Tasks to Improve Quantitative Reasoning in an Introductory Mathematics Course

ERIC Educational Resources Information Center

Kruse, Gerald; Drews, David

2013-01-01

A full-cycle assessment of our efforts to improve quantitative reasoning in an introductory math course is described. Our initial iteration substituted more open-ended performance tasks for the active learning projects than had been used. Using a quasi-experimental design, we compared multiple sections of the same course and found non-significant…

The More Things Change the More They Stay the Same

ERIC Educational Resources Information Center

Barnett, Cassandra

2015-01-01

Over the last century, school library standards have gone through a number of iterations, and expectations for student learning have evolved. While the school library of today looks and functions differently from the school library of the early 1900s, the vision surprisingly is still very similar. The goal of today's school library is to provide…

Examining the Characteristics of Student Postings That Are Liked and Linked in a CSCL Environment

ERIC Educational Resources Information Center

Makos, Alexandra; Lee, Kyungmee; Zingaro, Daniel

2015-01-01

This case study is the first iteration of a large-scale design-based research project to improve Pepper, an interactive discussion-based learning environment. In this phase, we designed and implemented two social features to scaffold positive learner interactivity behaviors: a "Like" button and linking tool. A mixed-methods approach was…

Stimulating Students' Use of External Representations for a Distance Education Time Machine Design

ERIC Educational Resources Information Center

Baaki, John; Luo, Tian

2017-01-01

As faculty members in an instructional design and technology (IDT) program, we wanted to help our graduate students better understand and experience how designers design in the real world. We aimed to design a reflective and collaborative learning environment where we sparked students to engage in reflection, ideation, and the iterative process of…

Using Mendeley to Support Collaborative Learning in the Classroom

ERIC Educational Resources Information Center

Khwaja, Tehmina; Eddy, Pamela L.

2015-01-01

The purpose of this study was to explore the use of Mendeley, a free online reference management and academic networking software, as a collaborative tool in the college classroom. Students in two iterations of a Graduate class used Mendeley to collaborate on a policy research project over the course of a semester. The project involved…

Design Features of a Friendly Software Environment for Novice Programmers. Technical Report No. 3.

ERIC Educational Resources Information Center

Eisenstadt, Marc

This paper describes the results of a 6-year period of design, implementation, testing, and iterative redesign of a programming language, user aids, and curriculum materials for use by psychology students learning how to write simple computer programs. The SOLO language, which was the resulting product, is primarily a simple, database…

Researchers Apply Lesson Study: A Cycle of Lesson Planning, Implementation, and Revision

ERIC Educational Resources Information Center

Regan, Kelley S.; Evmenova, Anya S.; Kurz, Leigh Ann; Hughes, Melissa D.; Sacco, Donna; Ahn, Soo Y.; MacVittie, Nichole; Good, Kevin; Boykin, Andrea; Schwartzer, Jessica; Chirinos, David S.

2016-01-01

Scripted lesson plans and/or professional development alone may not be sufficient to encourage teachers to reflect on the quality of their teaching and improve their teaching. One learning tool that teachers may use to improve their teaching is Lesson Study (LS). LS is a collaborative process involving educators, based on concepts of iteration and…

Complex Adaptive Systems and the Origins of Adaptive Structure: What Experiments Can Tell Us

ERIC Educational Resources Information Center

Cornish, Hannah; Tamariz, Monica; Kirby, Simon

2009-01-01

Language is a product of both biological and cultural evolution. Clues to the origins of key structural properties of language can be found in the process of cultural transmission between learners. Recent experiments have shown that iterated learning by human participants in the laboratory transforms an initially unstructured artificial language…

ESSA Offers Changes That Can Continue Learning Gains

ERIC Educational Resources Information Center

Chenoweth, Karin

2016-01-01

The Every Student Succeeds Act (ESSA) gives educators a fresh opportunity to think through how to ensure that all students achieve at high levels, especially kids from low-income families and kids of color. The new law continues with four principles that had been in earlier iterations, all of which have important implications for students from…

Community Indicators: A Framework for Observing and Supporting Community Activity on Cloudworks

ERIC Educational Resources Information Center

Galley, Rebecca; Conole, Gráinne; Alevizou, Panagiota

2014-01-01

Cloudworks (Cloudworks.ac.uk) is a social networking site designed for sharing, finding and discussing learning and teaching ideas and experiences. Design and development of the site has been based on an iterative analysis, development and implementation approach, underpinned by ongoing research and evaluation. To this end, we have been seeking to…

Conditions for the Effectiveness of a Tablet-Based Algebra Program

ERIC Educational Resources Information Center

Jaciw, Andrew P.; Toby, Megan; Ma, Boya

2012-01-01

Tablets such as the iPad represent the next iteration of technologies that hold promise to facilitate learning, particularly in mathematics. In the case of algebra, tablets have the potential to bring the curriculum to life by easily linking to supporting materials and they allow an interactive experience whereby manipulation of one type of…

Shifting Relations with the More-than-Human: Six Threshold Concepts for Transformative Sustainability Learning

ERIC Educational Resources Information Center

Barrett, M. J.; Harmin, Matthew; Maracle, Bryan; Patterson, Molly; Thomson, Christina; Flowers, Michelle; Bors, Kirk

2017-01-01

Using the iterative process of action research, we identify six portals of understanding, called threshold concepts, which can be used as curricular guideposts to disrupt the socially constituted separation, and hierarchy, between humans and the more-than-human. The threshold concepts identified in this study provide focal points for a curriculum…

Human factors engineering verification and validation for APR1400 computerized control room

DOE Office of Scientific and Technical Information (OSTI.GOV)

Shin, Y. C.; Moon, H. K.; Kim, J. H.

2006-07-01

This paper introduces the Advanced Power Reactor 1400 (APR1400) HFE V and V activities the Korea Hydro Nuclear Plant Co. LTD. (KHNP) has performed for the last 10 years and some of the lessons learned through these activities. The features of APR1400 main control room include large display panel, redundant compact workstations, computer-based procedure, and safety console. Several iterations of human factors evaluations have been performed from small scale proof of concept tests to large scale integrated system tests for identifying human engineering deficiencies in the human system interface design. Evaluations in the proof of concept test were focused onmore » checking the presence of any show stopper problems in the design concept. Later evaluations were mostly for finding design problems and for assuring the resolution of human factors issues of advanced control room. The results of design evaluations were useful not only for refining the control room design, but also for licensing the standard design. Several versions of APR1400 mock-ups with dynamic simulation models of currently operating Korea Standard Nuclear Plant (KSNP) have been used for the evaluations with the participation of operators from KSNP plants. (authors)« less

Physics and Engineering Design of the ITER Electron Cyclotron Emission Diagnostic

NASA Astrophysics Data System (ADS)

Rowan, W. L.; Austin, M. E.; Houshmandyar, S.; Phillips, P. E.; Beno, J. H.; Ouroua, A.; Weeks, D. A.; Hubbard, A. E.; Stillerman, J. A.; Feder, R. E.; Khodak, A.; Taylor, G.; Pandya, H. K.; Danani, S.; Kumar, R.

2015-11-01

Electron temperature (Te) measurements and consequent electron thermal transport inferences will be critical to the non-active phases of ITER operation and will take on added importance during the alpha heating phase. Here, we describe our design for the diagnostic that will measure spatial and temporal profiles of Te using electron cyclotron emission (ECE). Other measurement capability includes high frequency instabilities (e.g. ELMs, NTMs, and TAEs). Since results from TFTR and JET suggest that Thomson Scattering and ECE differ at high Te due to driven non-Maxwellian distributions, non-thermal features of the ITER electron distribution must be documented. The ITER environment presents other challenges including space limitations, vacuum requirements, and very high-neutron-fluence. Plasma control in ITER will require real-time Te. The diagnosic design that evolved from these sometimes-conflicting needs and requirements will be described component by component with special emphasis on the integration to form a single effective diagnostic system. Supported by PPPL/US-DA via subcontract S013464-C to UT Austin.

"Ask Ernö": a self-learning tool for assignment and prediction of nuclear magnetic resonance spectra.

PubMed

Castillo, Andrés M; Bernal, Andrés; Dieden, Reiner; Patiny, Luc; Wist, Julien

2016-01-01

We present "Ask Ernö", a self-learning system for the automatic analysis of NMR spectra, consisting of integrated chemical shift assignment and prediction tools. The output of the automatic assignment component initializes and improves a database of assigned protons that is used by the chemical shift predictor. In turn, the predictions provided by the latter facilitate improvement of the assignment process. Iteration on these steps allows Ask Ernö to improve its ability to assign and predict spectra without any prior knowledge or assistance from human experts. This concept was tested by training such a system with a dataset of 2341 molecules and their (1)H-NMR spectra, and evaluating the accuracy of chemical shift predictions on a test set of 298 partially assigned molecules (2007 assigned protons). After 10 iterations, Ask Ernö was able to decrease its prediction error by 17 %, reaching an average error of 0.265 ppm. Over 60 % of the test chemical shifts were predicted within 0.2 ppm, while only 5 % still presented a prediction error of more than 1 ppm. Ask Ernö introduces an innovative approach to automatic NMR analysis that constantly learns and improves when provided with new data. Furthermore, it completely avoids the need for manually assigned spectra. This system has the potential to be turned into a fully autonomous tool able to compete with the best alternatives currently available.Graphical abstractSelf-learning loop. Any progress in the prediction (forward problem) will improve the assignment ability (reverse problem) and vice versa.

The prefrontal cortex and hybrid learning during iterative competitive games.

PubMed

Abe, Hiroshi; Seo, Hyojung; Lee, Daeyeol

2011-12-01

Behavioral changes driven by reinforcement and punishment are referred to as simple or model-free reinforcement learning. Animals can also change their behaviors by observing events that are neither appetitive nor aversive when these events provide new information about payoffs available from alternative actions. This is an example of model-based reinforcement learning and can be accomplished by incorporating hypothetical reward signals into the value functions for specific actions. Recent neuroimaging and single-neuron recording studies showed that the prefrontal cortex and the striatum are involved not only in reinforcement and punishment, but also in model-based reinforcement learning. We found evidence for both types of learning, and hence hybrid learning, in monkeys during simulated competitive games. In addition, in both the dorsolateral prefrontal cortex and orbitofrontal cortex, individual neurons heterogeneously encoded signals related to actual and hypothetical outcomes from specific actions, suggesting that both areas might contribute to hybrid learning. © 2011 New York Academy of Sciences.

Active control for stabilization of neoclassical tearing modesa)

NASA Astrophysics Data System (ADS)

Humphreys, D. A.; Ferron, J. R.; La Haye, R. J.; Luce, T. C.; Petty, C. C.; Prater, R.; Welander, A. S.

2006-05-01

This work describes active control algorithms used by DIII-D [J. L. Luxon, Nucl. Fusion 42, 614 (2002)] to stabilize and maintain suppression of 3/2 or 2/1 neoclassical tearing modes (NTMs) by application of electron cyclotron current drive (ECCD) at the rational q surface. The DIII-D NTM control system can determine the correct q-surface/ECCD alignment and stabilize existing modes within 100-500ms of activation, or prevent mode growth with preemptive application of ECCD, in both cases enabling stable operation at normalized beta values above 3.5. Because NTMs can limit performance or cause plasma-terminating disruptions in tokamaks, their stabilization is essential to the high performance operation of ITER [R. Aymar et al., ITER Joint Central Team, ITER Home Teams, Nucl. Fusion 41, 1301 (2001)]. The DIII-D NTM control system has demonstrated many elements of an eventual ITER solution, including general algorithms for robust detection of q-surface/ECCD alignment and for real-time maintenance of alignment following the disappearance of the mode. This latter capability, unique to DIII-D, is based on real-time reconstruction of q-surface geometry by a Grad-Shafranov solver using external magnetics and internal motional Stark effect measurements. Alignment is achieved by varying either the plasma major radius (and the rational q surface) or the toroidal field (and the deposition location). The requirement to achieve and maintain q-surface/ECCD alignment with accuracy on the order of 1cm is routinely met by the DIII-D Plasma Control System and these algorithms. We discuss the integrated plasma control design process used for developing these and other general control algorithms, which includes physics-based modeling and testing of the algorithm implementation against simulations of actuator and plasma responses. This systematic design/test method and modeling environment enabled successful mode suppression by the NTM control system upon first-time use in an experimental discharge.

Robust non-fragile finite-frequency H∞ static output-feedback control for active suspension systems

NASA Astrophysics Data System (ADS)

Wang, Gang; Chen, Changzheng; Yu, Shenbo

2017-07-01

This paper deals with the problem of non-fragile H∞ static output-feedback control of vehicle active suspension systems with finite-frequency constraint. The control objective is to improve ride comfort within the given frequency range and ensure the hard constraints in the time-domain. Moreover, in order to enhance the robustness of the controller, the control gain perturbation is also considered in controller synthesis. Firstly, a new non-fragile H∞ finite-frequency control condition is established by using generalized Kalman-Yakubovich-Popov (GKYP) lemma. Secondly, the static output-feedback control gain is directly derived by using a non-iteration algorithm. Different from the existing iteration LMI results, the static output-feedback design is simple and less conservative. Finally, the proposed control algorithm is applied to a quarter-car active suspension model with actuator dynamics, numerical results are made to show the effectiveness and merits of the proposed method.

Status of Europe's contribution to the ITER EC system

NASA Astrophysics Data System (ADS)

Albajar, F.; Aiello, G.; Alberti, S.; Arnold, F.; Avramidis, K.; Bader, M.; Batista, R.; Bertizzolo, R.; Bonicelli, T.; Braunmueller, F.; Brescan, C.; Bruschi, A.; von Burg, B.; Camino, K.; Carannante, G.; Casarin, V.; Castillo, A.; Cauvard, F.; Cavalieri, C.; Cavinato, M.; Chavan, R.; Chelis, J.; Cismondi, F.; Combescure, D.; Darbos, C.; Farina, D.; Fasel, D.; Figini, L.; Gagliardi, M.; Gandini, F.; Gantenbein, G.; Gassmann, T.; Gessner, R.; Goodman, T. P.; Gracia, V.; Grossetti, G.; Heemskerk, C.; Henderson, M.; Hermann, V.; Hogge, J. P.; Illy, S.; Ioannidis, Z.; Jelonnek, J.; Jin, J.; Kasparek, W.; Koning, J.; Krause, A. S.; Landis, J. D.; Latsas, G.; Li, F.; Mazzocchi, F.; Meier, A.; Moro, A.; Nousiainen, R.; Purohit, D.; Nowak, S.; Omori, T.; van Oosterhout, J.; Pacheco, J.; Pagonakis, I.; Platania, P.; Poli, E.; Preis, A. K.; Ronden, D.; Rozier, Y.; Rzesnicki, T.; Saibene, G.; Sanchez, F.; Sartori, F.; Sauter, O.; Scherer, T.; Schlatter, C.; Schreck, S.; Serikov, A.; Siravo, U.; Sozzi, C.; Spaeh, P.; Spichiger, A.; Strauss, D.; Takahashi, K.; Thumm, M.; Tigelis, I.; Vaccaro, A.; Vomvoridis, J.; Tran, M. Q.; Weinhorst, B.

2015-03-01

The electron cyclotron (EC) system of ITER for the initial configuration is designed to provide 20MW of RF power into the plasma during 3600s and a duty cycle of up to 25% for heating and (co and counter) non-inductive current drive, also used to control the MHD plasma instabilities. The EC system is being procured by 5 domestic agencies plus the ITER Organization (IO). F4E has the largest fraction of the EC procurements, which includes 8 high voltage power supplies (HVPS), 6 gyrotrons, the ex-vessel waveguides (includes isolation valves and diamond windows) for all launchers, 4 upper launchers and the main control system. F4E is working with IO to improve the overall design of the EC system by integrating consolidated technological advances, simplifying the interfaces, and doing global engineering analysis and assessments of EC heating and current drive physics and technology capabilities. Examples are the optimization of the HVPS and gyrotron requirements and performance relative to power modulation for MHD control, common qualification programs for diamond window procurements, assessment of the EC grounding system, and the optimization of the launcher steering angles for improved EC access. Here we provide an update on the status of Europe's contribution to the ITER EC system, and a summary of the global activities underway by F4E in collaboration with IO for the optimization of the subsystems.

Robust Multimodal Dictionary Learning

PubMed Central

Cao, Tian; Jojic, Vladimir; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

2014-01-01

We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and re-finements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration. PMID:24505674